36 CFR 2.31 - Trespassing, tampering and vandalism.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 1 2012-07-01 2012-07-01 false Trespassing, tampering and... INTERIOR RESOURCE PROTECTION, PUBLIC USE AND RECREATION § 2.31 Trespassing, tampering and vandalism. (a) The following are prohibited: (1) Trespassing. Trespassing, entering or remaining in or upon property...

36 CFR 2.31 - Trespassing, tampering and vandalism.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 36 Parks, Forests, and Public Property 1 2010-07-01 2010-07-01 false Trespassing, tampering and... INTERIOR RESOURCE PROTECTION, PUBLIC USE AND RECREATION § 2.31 Trespassing, tampering and vandalism. (a) The following are prohibited: (1) Trespassing. Trespassing, entering or remaining in or upon property...

Tampered Region Localization of Digital Color Images Based on JPEG Compression Noise

NASA Astrophysics Data System (ADS)

Wang, Wei; Dong, Jing; Tan, Tieniu

With the availability of various digital image edit tools, seeing is no longer believing. In this paper, we focus on tampered region localization for image forensics. We propose an algorithm which can locate tampered region(s) in a lossless compressed tampered image when its unchanged region is output of JPEG decompressor. We find the tampered region and the unchanged region have different responses for JPEG compression. The tampered region has stronger high frequency quantization noise than the unchanged region. We employ PCA to separate different spatial frequencies quantization noises, i.e. low, medium and high frequency quantization noise, and extract high frequency quantization noise for tampered region localization. Post-processing is involved to get final localization result. The experimental results prove the effectiveness of our proposed method.

36 CFR 1002.31 - Trespassing, tampering and vandalism.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 3 2011-07-01 2011-07-01 false Trespassing, tampering and..., PUBLIC USE AND RECREATION § 1002.31 Trespassing, tampering and vandalism. (a) The following are prohibited: (1) Trespassing. Trespassing, entering or remaining in or upon property or real property not open...

36 CFR 1002.31 - Trespassing, tampering and vandalism.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 3 2012-07-01 2012-07-01 false Trespassing, tampering and..., PUBLIC USE AND RECREATION § 1002.31 Trespassing, tampering and vandalism. (a) The following are prohibited: (1) Trespassing. Trespassing, entering or remaining in or upon property or real property not open...

36 CFR 1002.31 - Trespassing, tampering and vandalism.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 3 2014-07-01 2014-07-01 false Trespassing, tampering and..., PUBLIC USE AND RECREATION § 1002.31 Trespassing, tampering and vandalism. (a) The following are prohibited: (1) Trespassing. Trespassing, entering or remaining in or upon property or real property not open...

36 CFR 1002.31 - Trespassing, tampering and vandalism.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false Trespassing, tampering and..., PUBLIC USE AND RECREATION § 1002.31 Trespassing, tampering and vandalism. (a) The following are prohibited: (1) Trespassing. Trespassing, entering or remaining in or upon property or real property not open...

A Secure and Robust Approach to Software Tamper Resistance

NASA Astrophysics Data System (ADS)

Ghosh, Sudeep; Hiser, Jason D.; Davidson, Jack W.

Software tamper-resistance mechanisms have increasingly assumed significance as a technique to prevent unintended uses of software. Closely related to anti-tampering techniques are obfuscation techniques, which make code difficult to understand or analyze and therefore, challenging to modify meaningfully. This paper describes a secure and robust approach to software tamper resistance and obfuscation using process-level virtualization. The proposed techniques involve novel uses of software check summing guards and encryption to protect an application. In particular, a virtual machine (VM) is assembled with the application at software build time such that the application cannot run without the VM. The VM provides just-in-time decryption of the program and dynamism for the application's code. The application's code is used to protect the VM to ensure a level of circular protection. Finally, to prevent the attacker from obtaining an analyzable snapshot of the code, the VM periodically discards all decrypted code. We describe a prototype implementation of these techniques and evaluate the run-time performance of applications using our system. We also discuss how our system provides stronger protection against tampering attacks than previously described tamper-resistance approaches.

Detection of Tampering Inconsistencies on Mobile Photos

NASA Astrophysics Data System (ADS)

Cao, Hong; Kot, Alex C.

Fast proliferation of mobile cameras and the deteriorating trust on digital images have created needs in determining the integrity of photos captured by mobile devices. As tampering often creates some inconsistencies, we propose in this paper a novel framework to statistically detect the image tampering inconsistency using accurately detected demosaicing weights features. By first cropping four non-overlapping blocks, each from one of the four quadrants in the mobile photo, we extract a set of demosaicing weights features from each block based on a partial derivative correlation model. Through regularizing the eigenspectrum of the within-photo covariance matrix and performing eigenfeature transformation, we further derive a compact set of eigen demosaicing weights features, which are sensitive to image signal mixing from different photo sources. A metric is then proposed to quantify the inconsistency based on the eigen weights features among the blocks cropped from different regions of the mobile photo. Through comparison, we show our eigen weights features perform better than the eigen features extracted from several other conventional sets of statistical forensics features in detecting the presence of tampering. Experimentally, our method shows a good confidence in tampering detection especially when one of the four cropped blocks is from a different camera model or brand with different demosaicing process.

Triboluminescent tamper-indicating device

DOEpatents

Johnston, Roger G.; Garcia, Anthony R. E.

2002-01-01

A tamper-indicating device is described. The device has a transparent or translucent cylindrical body that includes triboluminescent material, and an outer opaque layer that prevents ambient light from entering. A chamber in the body holds an undeveloped piece of photographic film bearing an image. The device is assembled from two body members. One of the body members includes a recess for storing film and an optical assembly that can be adjusted to prevent light from passing through the assembly and exposing the film. To use the device with a hasp, the body members are positioned on opposite sides of a hasp, inserted through the hasp, and attached. The optical assembly is then manipulated to allow any light generated from the triboluminescent materials during a tampering activity that damages the device to reach the film and destroy the image on the film.

A tamper-indicating quantum seal

DOE PAGES

Williams, Brian P.; Britt, Keith A.; Humble, Travis S.

2016-01-04

Technical means for identifying when tampering occurs is a critical part of many containment and surveillance technologies. Conventional fiber optic seals provide methods for monitoring enclosed inventories, but they are vulnerable to spoofing attacks based on classical physics. We address these vulnerabilities with the development of a quantum seal that offers the ability to detect the intercept-resend attack using quantum integrity verification. Our approach represents an application of entanglement to provide guarantees in the authenticity of the seal state by verifying it was transmitted coherently. We implement these ideas using polarization-entangled photon pairs that are verified after passing through amore » fiber-optic channel testbed. Using binary detection theory, we find the probability of detecting inauthentic signals is greater than 0.9999 with a false alarm chance of 10 –9 for a 10 second sampling interval. In addition, we show how the Hong-Ou-Mandel effect concurrently provides a tight bound on redirection attack, in which tampering modifies the shape of the seal. Our measurements limit the tolerable path length change to sub-millimeter disturbances. As a result, these tamper-indicating features of the quantum seal offer unprecedented security for unattended monitoring systems.« less

Unattended wireless proximity sensor networks for counterterrorism, force protection, littoral environments, PHM, and tamper monitoring ground applications

NASA Astrophysics Data System (ADS)

Forcier, Bob

2003-09-01

This paper describes a digital-ultrasonic ground network, which forms an unique "unattended mote sensor system" for monitoring the environment, personnel, facilities, vehicles, power generation systems or aircraft in Counter-Terrorism, Force Protection, Prognostic Health Monitoring (PHM) and other ground applications. Unattended wireless smart sensor/tags continuously monitor the environment and provide alerts upon changes or disruptions to the environment. These wireless smart sensor/tags are networked utilizing ultrasonic wireless motes, hybrid RF/Ultrasonic Network Nodes and Base Stations. The network is monitored continuously with a 24/7 remote and secure monitoring system. This system utilizes physical objects such as a vehicle"s structure or a building to provide the media for two way secure communication of key metrics and sensor data and eliminates the "blind spots" that are common in RF solutions because of structural elements of buildings, etc. The digital-ultrasonic sensors have networking capability and a 32-bit identifier, which provide a platform for a robust data acquisition (DAQ) for a large amount of sensors. In addition, the network applies a unique "signature" of the environment by comparing sensor-to-sensor data to pick up on minute changes, which would signal an invasion of unknown elements or signal a potential tampering in equipment or facilities. The system accommodates satellite and other secure network uplinks in either RF or UWB protocols. The wireless sensors can be dispersed by ground or air maneuvers. In addition, the sensors can be incorporated into the structure or surfaces of vehicles, buildings, or clothing of field personnel.

Blind technique using blocking artifacts and entropy of histograms for image tampering detection

NASA Astrophysics Data System (ADS)

Manu, V. T.; Mehtre, B. M.

2017-06-01

The tremendous technological advancements in recent times has enabled people to create, edit and circulate images easily than ever before. As a result of this, ensuring the integrity and authenticity of the images has become challenging. Malicious editing of images to deceive the viewer is referred to as image tampering. A widely used image tampering technique is image splicing or compositing, in which regions from different images are copied and pasted. In this paper, we propose a tamper detection method utilizing the blocking and blur artifacts which are the footprints of splicing. The classification of images as tampered or not, is done based on the standard deviations of the entropy histograms and block discrete cosine transformations. We can detect the exact boundaries of the tampered area in the image, if the image is classified as tampered. Experimental results on publicly available image tampering datasets show that the proposed method outperforms the existing methods in terms of accuracy.

Ephemeral profiles of prescription drug and formulation tampering: evolving pseudoscience on the Internet.

PubMed

Cone, Edward J

2006-06-01

The magnitude of non-therapeutic use, or misuse of prescription pharmaceuticals now rivals that of illicit drug abuse. Drug and formulation tampering enables misusers to administer higher doses by intended and non-intended routes. Perceived motives appear to be a combination of interests in achieving a faster onset and enhancing psychoactive effects. Narcotic analgesics, stimulants, and depressants are widely sought, examined, and tampered with for recreational use. This review examines tampering methods reported on the Internet for selected pharmaceutical products. The Internet provides broad and varied guidance on tampering methods that are specific to drug classes and unique formulations. Instructions are available on crushing, separating, purifying and chemically altering specific formulations to allow changes in dosage, route of administration, and time course of effects. Many pharmaceutical formulations contain features that serve as "barriers" to tampering. The nature and effectiveness of formulation barriers vary widely with many being overcome by adventurous misusers. Examples of successes and failures in tampering attempts are frequently described on Internet sites that support recreational drug use. Successful tampering methods that have widespread appeal evolve into recipes and become archived on multiple websites. Examples of tampering methods include: (1) how to separate narcotic drugs (codeine, hydrocodone, oxycodone) from excipients and non-desirable actives (aspirin, acetaminophen, ibuprofen); (2) overcoming time-release formulations (beads, layers, matrices); (3) removal of active drug from high-dose formulations (patches, pills); (4) alteration of dosage forms for alternate routes of administration. The development of successful formulations that inhibit or prevent drug/formulation tampering with drugs of abuse should take into consideration the scope and practice of tampering methods available to recreational drug users on the Internet.

36 CFR § 1002.31 - Trespassing, tampering and vandalism.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 3 2013-07-01 2012-07-01 true Trespassing, tampering and... PROTECTION, PUBLIC USE AND RECREATION § 1002.31 Trespassing, tampering and vandalism. (a) The following are prohibited: (1) Trespassing. Trespassing, entering or remaining in or upon property or real property not open...

The Social Construction of the Urban Use of Information Technology: The Case of Tampere, Finland

ERIC Educational Resources Information Center

Inkinen, Tommi

2006-01-01

This paper explores the social use of information and communication technologies (ICTs) in the city of Tampere, Finland. It focuses on two essential elements: the city (as the location with national context) and citizens (as members of the "local" information society). The paper also examines the question of building social networks via…

50 CFR 27.65 - Tampering with vehicles and equipment.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 50 Wildlife and Fisheries 8 2011-10-01 2011-10-01 false Tampering with vehicles and equipment. 27.65 Section 27.65 Wildlife and Fisheries UNITED STATES FISH AND WILDLIFE SERVICE, DEPARTMENT OF THE INTERIOR (CONTINUED) THE NATIONAL WILDLIFE REFUGE SYSTEM PROHIBITED ACTS Disturbing Violations: Against Nonwildlife Property § 27.65 Tampering with...

50 CFR 27.65 - Tampering with vehicles and equipment.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 50 Wildlife and Fisheries 9 2012-10-01 2012-10-01 false Tampering with vehicles and equipment. 27.65 Section 27.65 Wildlife and Fisheries UNITED STATES FISH AND WILDLIFE SERVICE, DEPARTMENT OF THE INTERIOR (CONTINUED) THE NATIONAL WILDLIFE REFUGE SYSTEM PROHIBITED ACTS Disturbing Violations: Against Nonwildlife Property § 27.65 Tampering with...

Evidence of tampering in watermark identification

NASA Astrophysics Data System (ADS)

McLauchlan, Lifford; Mehrübeoglu, Mehrübe

2009-08-01

In this work, watermarks are embedded in digital images in the discrete wavelet transform (DWT) domain. Principal component analysis (PCA) is performed on the DWT coefficients. Next higher order statistics based on the principal components and the eigenvalues are determined for different sets of images. Feature sets are analyzed for different types of attacks in m dimensional space. The results demonstrate the separability of the features for the tampered digital copies. Different feature sets are studied to determine more effective tamper evident feature sets. The digital forensics, the probable manipulation(s) or modification(s) performed on the digital information can be identified using the described technique.

A graphite oxide (GO)-based remote readable tamper evident seal

DOE PAGES

Cattaneo, Alessandro; Bossert, Jason Andrew; Guzman, Christian; ...

2016-09-08

Here, this paper presents a prototype of a remotely readable graphite oxide (GO) paper-based tamper evident seal. The proposed device combines the tunable electrical properties offered by reduced graphite oxide (RGO) with a compressive sampling scheme. The benefit of using RGO as a tamper evident seal material is the sensitivity of its electrical properties to the common mechanisms adopted to defeat tamper-evident seals. RGO’s electrical properties vary upon local stress or cracks induced by mechanical action (e.g., produced by shimming or lifting attacks). Further, modification of the seal’s electrical properties can result from the incidence of other defeat mechanisms, suchmore » as temperature changes, solvent treatment and steam application. The electrical tunability of RGO enables the engraving of a circuit on the area of the tamper evident seal intended to be exposed to malicious attacks. The operation of the tamper evident seal, as well as its remote communication functionality, is supervised by a microcontroller unit (MCU). The MCU uses the RGO-engraved circuitry to physically implement a compressive sampling acquisition procedure. The compressive sampling scheme provides the seal with self-authentication and self-state-of-health awareness capabilities. Finally, the prototype shows potential for use in low-power, embedded, remote-operation nonproliferation security related applications.« less

Security protection of DICOM medical images using dual-layer reversible watermarking with tamper detection capability.

PubMed

Tan, Chun Kiat; Ng, Jason Changwei; Xu, Xiaotian; Poh, Chueh Loo; Guan, Yong Liang; Sheah, Kenneth

2011-06-01

Teleradiology applications and universal availability of patient records using web-based technology are rapidly gaining importance. Consequently, digital medical image security has become an important issue when images and their pertinent patient information are transmitted across public networks, such as the Internet. Health mandates such as the Health Insurance Portability and Accountability Act require healthcare providers to adhere to security measures in order to protect sensitive patient information. This paper presents a fully reversible, dual-layer watermarking scheme with tamper detection capability for medical images. The scheme utilizes concepts of public-key cryptography and reversible data-hiding technique. The scheme was tested using medical images in DICOM format. The results show that the scheme is able to ensure image authenticity and integrity, and to locate tampered regions in the images.

25 CFR 11.440 - Tampering with or fabricating physical evidence.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 25 Indians 1 2014-04-01 2014-04-01 false Tampering with or fabricating physical evidence. 11.440 Section 11.440 Indians BUREAU OF INDIAN AFFAIRS, DEPARTMENT OF THE INTERIOR LAW AND ORDER COURTS OF INDIAN OFFENSES AND LAW AND ORDER CODE Criminal Offenses § 11.440 Tampering with or fabricating physical evidence...

25 CFR 11.440 - Tampering with or fabricating physical evidence.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 25 Indians 1 2011-04-01 2011-04-01 false Tampering with or fabricating physical evidence. 11.440 Section 11.440 Indians BUREAU OF INDIAN AFFAIRS, DEPARTMENT OF THE INTERIOR LAW AND ORDER COURTS OF INDIAN OFFENSES AND LAW AND ORDER CODE Criminal Offenses § 11.440 Tampering with or fabricating physical evidence...

Tamper-indicating device having a glass body

DOEpatents

Johnston, Roger G.; Garcia, Anthony R. E.

2003-04-29

A tamper-indicating device is described. The device has a first glass body member and a second glass body member that are attached to each other through a hasp. The glass body members of the device can be tempered. The body members can be configured with hollow volumes into which powders, microparticles, liquids, gels, or combinations thereof are sealed. The choice, the amount, and the location of these materials can produce a visible, band pattern to provide each body member with a unique fingerprint identifier, which makes it extremely difficult to repair or replace once it is damaged in order to avoid tamper detection.

Secure RFID tag or sensor with self-destruction mechanism upon tampering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nekoogar, Faranak; Dowla, Farid; Twogood, Richard

A circuit board anti-tamper mechanism comprises a circuit board having a frangible portion, a trigger having a trigger spring, a trigger arming mechanism actuated by the trigger wherein the trigger arming mechanism is initially non-actuated, a force producing mechanism, a latch providing mechanical communication between the trigger arming mechanism and the force producing mechanism, wherein the latch initially retains the force producing mechanism in a refracted position. Arming pressure applied to the trigger sufficient to overcome the trigger spring force will actuate the trigger arming mechanism, causing the anti-tamper mechanism to be armed. Subsequent tampering with the anti-tamper mechanism resultsmore » in a decrease of pressure on the trigger below the trigger spring force, thereby causing the trigger arming mechanism to actuate the latch, thereby releasing the force producing mechanism to apply force to the frangible portion of the circuit board, thereby breaking the circuit board.« less

Medical Image Tamper Detection Based on Passive Image Authentication.

PubMed

Ulutas, Guzin; Ustubioglu, Arda; Ustubioglu, Beste; V Nabiyev, Vasif; Ulutas, Mustafa

2017-12-01

Telemedicine has gained popularity in recent years. Medical images can be transferred over the Internet to enable the telediagnosis between medical staffs and to make the patient's history accessible to medical staff from anywhere. Therefore, integrity protection of the medical image is a serious concern due to the broadcast nature of the Internet. Some watermarking techniques are proposed to control the integrity of medical images. However, they require embedding of extra information (watermark) into image before transmission. It decreases visual quality of the medical image and can cause false diagnosis. The proposed method uses passive image authentication mechanism to detect the tampered regions on medical images. Structural texture information is obtained from the medical image by using local binary pattern rotation invariant (LBPROT) to make the keypoint extraction techniques more successful. Keypoints on the texture image are obtained with scale invariant feature transform (SIFT). Tampered regions are detected by the method by matching the keypoints. The method improves the keypoint-based passive image authentication mechanism (they do not detect tampering when the smooth region is used for covering an object) by using LBPROT before keypoint extraction because smooth regions also have texture information. Experimental results show that the method detects tampered regions on the medical images even if the forged image has undergone some attacks (Gaussian blurring/additive white Gaussian noise) or the forged regions are scaled/rotated before pasting.

DEVELOPMENT OF A CERAMIC TAMPER INDICATING SEAL: SRNL CONTRIBUTIONS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krementz, D.; Brinkman, K.; Martinez-Rodriguez, M.

2013-06-03

Savannah River National Laboratory (SRNL) and Sandia National Laboratories (SNL) are collaborating on development of a Ceramic Seal, also sometimes designated the Intrinsically Tamper Indicating Ceramic Seal (ITICS), which is a tamper indicating seal for international safeguards applications. The Ceramic Seal is designed to be a replacement for metal loop seals that are currently used by the IAEA and other safeguards organizations. The Ceramic Seal has numerous features that enhance the security of the seal, including a frangible ceramic body, protective and tamper indicating coatings, an intrinsic unique identifier using Laser Surface Authentication, electronics incorporated into the seal that providemore » cryptographic seal authentication, and user-friendly seal wire capture. A second generation prototype of the seal is currently under development whose seal body is of Low Temperature Co-fired Ceramic (LTCC) construction. SRNL has developed the mechanical design of the seal in an iterative process incorporating comments from the SNL vulnerability review team. SRNL is developing fluorescent tamper indicating coatings, with recent development focusing on optimizing the durability of the coatings and working with a vendor to develop a method to apply coatings on a 3-D surface. SRNL performed a study on the effects of radiation on the electronics of the seal and possible radiation shielding techniques to minimize the effects. SRNL is also investigating implementation of Laser Surface Authentication (LSA) as a means of unique identification of each seal and the effects of the surface coatings on the LSA signature.« less

40 CFR 205.173-2 - Tampering.

Code of Federal Regulations, 2010 CFR

2010-07-01

... removal or puncturing the muffler, baffles, header pipes, or any other component which conducts exhaust... EQUIPMENT NOISE EMISSION CONTROLS Motorcycle Exhaust Systems § 205.173-2 Tampering. The manufacturer must... exhaust system which causes the motorcycle to exceed the Federal noise standard. Use of the motorcycle...

Evaluation of the resistance of a geopolymer-based drug delivery system to tampering.

PubMed

Cai, Bing; Engqvist, Håkan; Bredenberg, Susanne

2014-04-25

Tamper-resistance is an important property of controlled-release formulations of opioid drugs. Tamper-resistant formulations aim to increase the degree of effort required to override the controlled release of the drug molecules from extended-release formulations for the purpose of non-medical use. In this study, the resistance of a geopolymer-based formulation to tampering was evaluated by comparing it with a commercial controlled-release tablet using several methods commonly used by drug abusers. Because of its high compressive strength and resistance to heat, much more effort and time was required to extract the drug from the geopolymer-based formulation. Moreover, in the drug-release test, the geopolymer-based formulation maintained its controlled-release characteristics after milling, while the drug was released immediately from the milled commercial tablets, potentially resulting in dose dumping. Although the tampering methods used in this study does not cover all methods that abuser could access, the results obtained by the described methods showed that the geopolymer matrix increased the degree of effort required to override the controlled release of the drug, suggesting that the formulation has improved resistance to some common drug-abuse tampering methods. The geopolymer matrix has the potential to make the opioid product less accessible and attractive to non-medical users. Copyright © 2014 Elsevier B.V. All rights reserved.

49 CFR Appendix C to Part 218 - Statement of Agency Enforcement Policy on Tampering

Code of Federal Regulations, 2014 CFR

2014-10-01

... Tampering C Appendix C to Part 218 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION, DEPARTMENT OF TRANSPORTATION RAILROAD OPERATING PRACTICES Pt. 218, App. C Appendix C to Part 218—Statement of Agency Enforcement Policy on Tampering The Rail Safety Improvement Act...

49 CFR Appendix C to Part 218 - Statement of Agency Enforcement Policy on Tampering

Code of Federal Regulations, 2013 CFR

2013-10-01

... Tampering C Appendix C to Part 218 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION, DEPARTMENT OF TRANSPORTATION RAILROAD OPERATING PRACTICES Pt. 218, App. C Appendix C to Part 218—Statement of Agency Enforcement Policy on Tampering The Rail Safety Improvement Act...

49 CFR Appendix C to Part 218 - Statement of Agency Enforcement Policy on Tampering

Code of Federal Regulations, 2010 CFR

2010-10-01

... Tampering C Appendix C to Part 218 Transportation Other Regulations Relating to Transportation (Continued) FEDERAL RAILROAD ADMINISTRATION, DEPARTMENT OF TRANSPORTATION RAILROAD OPERATING PRACTICES Pt. 218, App. C Appendix C to Part 218—Statement of Agency Enforcement Policy on Tampering The Rail Safety Improvement Act...

Optically resonant subwavelength films for tamper-indicating tags and seals

NASA Astrophysics Data System (ADS)

Alvine, Kyle J.; Suter, Jonathan D.; Bernacki, Bruce E.; Bennett, Wendy D.

2015-05-01

We present the design, modeling and performance of a proof-of-concept tamper indicating approach that exploits newlydeveloped subwavelength-patterned films. These films have a nanostructure-dependent resonant optical reflection that is wavelength, angle, and polarization dependent. As such, they can be tailored to fabricate overlay transparent films for tamper indication and authentication of sensitive or controlled materials not possible with currently-known technologies. An additional advantage is that the unique optical signature is dictated by the geometry and fabrication process of the nanostructures in the film, rather than on the material used. The essential structure unit in the subwavelength resonant coating is a nanoscale Open-Ring Resonator (ORR). This building block is fabricated by coating a dielectric nanoscale template with metal to form a hemispherical shell-like structure. This curved metallic shell structure has a cross-section with an intrinsic capacitance and inductance and is thus the optical equivalent to the well-known "LC" circuit where the capacitance and inductance are determined by the nanoshell dimensions. For structures with sub 100 nm scale, this resonance occurs in the visible electromagnetic spectrum, and in the IR for larger shells. Tampering of the film would be visible though misalignment of the angle-sensitive features in the film. It is additionally possible to add in intrinsic oxidation and strain sensitive matrix materials to further complicate tamper repair and counterfeiting. Cursory standoff readout would be relatively simple using a combination of a near-infrared (or visible) LED flashlight and polarizer or passively using room lighting illumination and a dispersive detector.

Ranking of sabotage/tampering avoidance technology alternatives

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andrews, W.B.; Tabatabai, A.S.; Powers, T.B.

1986-01-01

Pacific Northwest Laboratory conducted a study to evaluate alternatives to the design and operation of nuclear power plants, emphasizing a reduction of their vulnerability to sabotage. Estimates of core melt accident frequency during normal operations and from sabotage/tampering events were used to rank the alternatives. Core melt frequency for normal operations was estimated using sensitivity analysis of results of probabilistic risk assessments. Core melt frequency for sabotage/tampering was estimated by developing a model based on probabilistic risk analyses, historic data, engineering judgment, and safeguards analyses of plant locations where core melt events could be initiated. Results indicate the most effectivemore » alternatives focus on large areas of the plant, increase safety system redundancy, and reduce reliance on single locations for mitigation of transients. Less effective options focus on specific areas of the plant, reduce reliance on some plant areas for safe shutdown, and focus on less vulnerable targets.« less

Active Time Domain Reflectometry for Tamper Indication in Unattended Safeguards Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sheen, David M.; Smith, Leon E.; Tedeschi, Jonathan R.

2015-07-14

The International Atomic Energy Agency (IAEA) continues to expand its use of unattended measurement systems. An increasing number of systems and an expanding family of instruments create challenges in terms of deployment efficiency and the implementation of data authentication measures. In collaboration with the IAEA, tamper-indicating measures to address data-transmission authentication challenges with unattended safeguards systems are under investigation. Pacific Northwest National Laboratory is studying the viability of active time-domain reflectometry (TDR) along two parallel but interconnected paths: (1) swept-frequency TDR as the highly flexible, laboratory gold standard to which field-deployable options can be compared, and (2) a low-cost commerciallymore » available spread-spectrum TDR technology as one option for field implementation. This paper describes the TDR methods under investigation and the associated benchtop test-bed, tampering scenarios of interest,, and viability measurement results to date (e.g., comparison of relative sensitivity to tamper scenarios).« less

Adolescent pedometer protocols: examining reactivity, tampering and participants' perceptions.

PubMed

Scott, Joseph John; Morgan, Philip James; Plotnikoff, Ronald Cyril; Trost, Stewart Graeme; Lubans, David Revalds

2014-01-01

The aim of this study was to investigate adolescents' potential reactivity and tampering while wearing pedometers by comparing different monitoring protocols to accelerometer output. The sample included adolescents (N = 123, age range = 14-15 years) from three secondary schools in New South Wales, Australia. Schools were randomised to one of the three pedometer monitoring protocols: (i) daily sealed (DS) pedometer group, (ii) unsealed (US) pedometer group or (iii) weekly sealed (WS) pedometer group. Participants wore pedometers (Yamax Digi-Walker CW700, Yamax Corporation, Kumamoto City, Japan) and accelerometers (Actigraph GT3X+, Pensacola, USA) simultaneously for seven days. Repeated measures analysis of variance was used to examine potential reactivity. Bivariate correlations between step counts and accelerometer output were calculated to explore potential tampering. The correlation between accelerometer output and pedometer steps/day was strongest among participants in the WS group (r = 0.82, P ≤ 0.001), compared to the US (r = 0.63, P ≤ 0.001) and DS (r = 0.16, P = 0.324) groups. The DS (P ≤ 0.001) and US (P = 0.003), but not the WS (P = 0.891), groups showed evidence of reactivity. The results suggest that reactivity and tampering does occur in adolescents and contrary to existing research, pedometer monitoring protocols may influence participant behaviour.

Tamper to delay motion and decrease ionization of a sample during short pulse x-ray imaging

DOEpatents

London, Richard A [Orinda, CA; Szoke,; Abraham, Hau-Riege [Fremont, CA; Stefan P. , Chapman; Henry, N [Livermore, CA

2007-06-26

A system for x-ray imaging of a small sample comprising positioning a tamper so that it is operatively connected to the sample, directing short intense x-ray pulses onto the tamper and the sample, and detecting an image from the sample. The tamper delays the explosive motion of the sample during irradiation by the short intense x-ray pulses, thereby extending the time to obtain an x-ray image of the original structure of the sample.

21 CFR 211.132 - Tamper-evident packaging requirements for over-the-counter (OTC) human drug products.

Code of Federal Regulations, 2014 CFR

2014-04-01

... dermatological, dentifrice, insulin, or lozenge product) for retail sale that is not packaged in a tamper..., dentifrice, insulin, or lozenge product) for retail sale shall package the product in a tamper-evident...

21 CFR 211.132 - Tamper-evident packaging requirements for over-the-counter (OTC) human drug products.

Code of Federal Regulations, 2012 CFR

2012-04-01

... dermatological, dentifrice, insulin, or lozenge product) for retail sale that is not packaged in a tamper..., dentifrice, insulin, or lozenge product) for retail sale shall package the product in a tamper-evident...

21 CFR 211.132 - Tamper-evident packaging requirements for over-the-counter (OTC) human drug products.

Code of Federal Regulations, 2013 CFR

2013-04-01

... dermatological, dentifrice, insulin, or lozenge product) for retail sale that is not packaged in a tamper..., dentifrice, insulin, or lozenge product) for retail sale shall package the product in a tamper-evident...

21 CFR 211.132 - Tamper-evident packaging requirements for over-the-counter (OTC) human drug products.

Code of Federal Regulations, 2010 CFR

2010-04-01

... dermatological, dentifrice, insulin, or lozenge product) for retail sale that is not packaged in a tamper..., dentifrice, insulin, or lozenge product) for retail sale shall package the product in a tamper-evident...

21 CFR 211.132 - Tamper-evident packaging requirements for over-the-counter (OTC) human drug products.

Code of Federal Regulations, 2011 CFR

2011-04-01

... dermatological, dentifrice, insulin, or lozenge product) for retail sale that is not packaged in a tamper..., dentifrice, insulin, or lozenge product) for retail sale shall package the product in a tamper-evident...

Detecting Test Tampering Using Item Response Theory

ERIC Educational Resources Information Center

Wollack, James A.; Cohen, Allan S.; Eckerly, Carol A.

2015-01-01

Test tampering, especially on tests for educational accountability, is an unfortunate reality, necessitating that the state (or its testing vendor) perform data forensic analyses, such as erasure analyses, to look for signs of possible malfeasance. Few statistical approaches exist for detecting fraudulent erasures, and those that do largely do not…

Test-Tampering Found Rampant in Atlanta System

ERIC Educational Resources Information Center

Samuels, Christina A.

2011-01-01

The author reports on a state investigation into Atlanta's impressive gains on state tests which finds that test-tampering was rampant in the much-praised school system. The report unveiled by the Georgia governor's office states that Atlanta teachers and principals for years methodically altered answer sheets for students taking state tests,…

Using Focus Groups to Study Consumer Understanding and Experiences with Tamper-Evident Packaging Devices

ERIC Educational Resources Information Center

Pascall, Melvin A.; Lee, Ken; Fraser, Angela; Halim, Linna

2009-01-01

A focus group with an educational component was used to help initiate a new research hypothesis. Early-stage development of a new tamper-evident invention was improved with input from a consumer focus group. The focus group comprised consumers who were shown several tamper-evident devices, including a new color-changing cap under active…

Community structure in networks

NASA Astrophysics Data System (ADS)

Newman, Mark

2004-03-01

Many networked systems, including physical, biological, social, and technological networks, appear to contain ``communities'' -- groups of nodes within which connections are dense, but between which they are sparser. The ability to find such communities in an automated fashion could be of considerable use. Communities in a web graph for instance might correspond to sets of web sites dealing with related topics, while communities in a biochemical network or an electronic circuit might correspond to functional units of some kind. We present a number of new methods for community discovery, including methods based on ``betweenness'' measures and methods based on modularity optimization. We also give examples of applications of these methods to both computer-generated and real-world network data, and show how our techniques can be used to shed light on the sometimes dauntingly complex structure of networked systems.

Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction: COMMUNITY DATA-DRIVEN METABOLIC NETWORK MODELING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Henry, Christopher S.; Bernstein, Hans C.; Weisenhorn, Pamela

Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the networkmore » reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources.« less

A comparison among tapentadol tamper-resistant formulations (TRF) and OxyContin® (non-TRF) in prescription opioid abusers

PubMed Central

Vosburg, Suzanne K.; Jones, Jermaine D.; Manubay, Jeanne M.; Ashworth, Judy B.; Shapiro, Douglas Y.; Comer, Sandra D.

2013-01-01

Aims To examine whether tamper-resistant formulations (TRFs) of tapentadol hydrochloride ER 50 mg (TAP50) and tapentadol hydrochloride 250 mg (TAP250) could be converted into forms amenable to intranasal (Study 1) or intravenous abuse (Study 2). Design Randomized, repeated-measures study designs were employed. A non-TRF of OxyContin® 40 mg (OXY40) served as a positive control. No drug was taken in either study. Setting The studies took place in an outpatient setting in New York, NY. Participants 25 experienced, healthy extended-release oxycodone abusers participated in each study. Measurements The primary outcome for Study 1 was percentage of participants who indicated they would snort the tampered tablets, while the primary outcome for Study 2 was percent yield of active drug in solution. Other descriptive variables such as time spent manipulating the tablets were also examined to better characterize tampering behaviors. Findings Tampered TRF tablets were less desirable than the tampered OXY40 tablets. Few individuals were willing to snort the TRF particles (TAP50: 24%, TAP250: 16%; OXY40: 100% p<.001). There was less drug extracted from the TAP50 tablet than from the OXY40 tablet (3.5% vs. 37.0%, p=.008), and no samples from the TAP250 tablets contained analyzable solutions of the drug. It took participants longer to tamper with the TAPs (Study 1: TAP50 vs. OXY40, p<.01; TAP250 vs. OXY40, p<.01; Study 2: TAP250 vs. OXY40, p<05). Conclusions Taptentadol TRF tablets were not well-liked by individuals who regularly tampered with extended-release oxycodone tablets. Employing tamper resistant technology may be a promising approach towards reducing the abuse potential of tapentadol ER. PMID:23316699

Community Wireless Networks

ERIC Educational Resources Information Center

Feld, Harold

2005-01-01

With increasing frequency, communities are seeing the arrival of a new class of noncommercial broadband providers: community wireless networks (CWNs). Utilizing the same wireless technologies that many colleges and universities have used to create wireless networks on campus, CWNs are creating broadband access for free or at costs well below…

Community detection using preference networks

NASA Astrophysics Data System (ADS)

Tasgin, Mursel; Bingol, Haluk O.

2018-04-01

Community detection is the task of identifying clusters or groups of nodes in a network where nodes within the same group are more connected with each other than with nodes in different groups. It has practical uses in identifying similar functions or roles of nodes in many biological, social and computer networks. With the availability of very large networks in recent years, performance and scalability of community detection algorithms become crucial, i.e. if time complexity of an algorithm is high, it cannot run on large networks. In this paper, we propose a new community detection algorithm, which has a local approach and is able to run on large networks. It has a simple and effective method; given a network, algorithm constructs a preference network of nodes where each node has a single outgoing edge showing its preferred node to be in the same community with. In such a preference network, each connected component is a community. Selection of the preferred node is performed using similarity based metrics of nodes. We use two alternatives for this purpose which can be calculated in 1-neighborhood of nodes, i.e. number of common neighbors of selector node and its neighbors and, the spread capability of neighbors around the selector node which is calculated by the gossip algorithm of Lind et.al. Our algorithm is tested on both computer generated LFR networks and real-life networks with ground-truth community structure. It can identify communities accurately in a fast way. It is local, scalable and suitable for distributed execution on large networks.

50 CFR 27.65 - Tampering with vehicles and equipment.

Code of Federal Regulations, 2010 CFR

2010-10-01

... INTERIOR (CONTINUED) THE NATIONAL WILDLIFE REFUGE SYSTEM PROHIBITED ACTS Disturbing Violations: Against... motor vehicle, boat, equipment or machinery or attempting to tamper with, enter, or start any motor vehicle, boat, equipment or machinery on any national wildlife refuge without proper authorization is...

Tamper-indicating quantum optical seals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Humble, Travis S; Williams, Brian P

2015-01-01

Confidence in the means for identifying when tampering occurs is critical for containment and surveillance technologies. Fiber-optic seals have proven especially useful for actively surveying large areas or inventories due to the extended transmission range and flexible layout of fiber. However, it is reasonable to suspect that an intruder could tamper with a fiber-optic sensor by accurately replicating the light transmitted through the fiber. In this contribution, we demonstrate a novel approach to using fiber-optic seals for safeguarding large-scale inventories with increased confidence in the state of the seal. Our approach is based on the use of quantum mechanical phenomenamore » to offer unprecedented surety in the authentication of the seal state. In particular, we show how quantum entangled photons can be used to monitor the integrity of a fiber-optic cable - the entangled photons serve as active sensing elements whose non-local correlations indicate normal seal operation. Moreover, we prove using the quantum no-cloning theorem that attacks against the quantum seal necessarily disturb its state and that these disturbances are immediately detected. Our quantum approach to seal authentication is based on physical principles alone and does not require the use of secret or proprietary information to ensure proper operation. We demonstrate an implementation of the quantum seal using a pair of entangled photons and we summarize our experimental results including the probability of detecting intrusions and the overall stability of the system design. We conclude by discussing the use of both free-space and fiber-based quantum seals for surveying large areas and inventories.« less

Network-Based Community Brings forth Sustainable Society

NASA Astrophysics Data System (ADS)

Kikuchi, Toshiko

It has already been shown that an artificial society based on the three relations of social configuration (market, communal, and obligatory relations) functioning in balance with each other formed a sustainable society which the social reproduction is possible. In this artificial society model, communal relations exist in a network-based community with alternating members rather than a conventional community with cooperative mutual assistance practiced in some agricultural communities. In this paper, using the comparison between network-based communities with alternating members and conventional communities with fixed members, the significance of a network-based community is considered. In concrete terms, the difference in appearance rate for sustainable society, economic activity and asset inequality between network-based communities and conventional communities is analyzed. The appearance rate for a sustainable society of network-based community is higher than that of conventional community. Moreover, most of network-based communities had a larger total number of trade volume than conventional communities. But, the value of Gini coefficient in conventional community is smaller than that of network-based community. These results show that communal relations based on a network-based community is significant for the social reproduction and economic efficiency. However, in such an artificial society, the inequality is sacrificed.

The Community Networking Handbook.

ERIC Educational Resources Information Center

Bajjaly, Stephen T.

This publication outlines the complete community networking process: planning, developing partnerships, funding, marketing, content, public access, and evaluation, and discusses the variety of roles that the local public library can play in this process. Chapter One, "The Importance of Community Networking," describes the importance of community…

A Theorem and its Application to Finite Tampers

DOE R&D Accomplishments Database

Feynman, R. P.

1946-08-15

A theorem is derived which is useful in the analysis of neutron problems in which all neutrons have the same velocity. It is applied to determine extrapolated end-points, the asymptotic amplitude from a point source, and the neutron density at the surface of a medium. Formulas fro the effect of finite tampers are derived by its aid, and their accuracy discussed.

Finding overlapping communities in multilayer networks.

PubMed

Liu, Weiyi; Suzumura, Toyotaro; Ji, Hongyu; Hu, Guangmin

2018-01-01

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks.

A network function-based definition of communities in complex networks.

PubMed

Chauhan, Sanjeev; Girvan, Michelle; Ott, Edward

2012-09-01

We consider an alternate definition of community structure that is functionally motivated. We define network community structure based on the function the network system is intended to perform. In particular, as a specific example of this approach, we consider communities whose function is enhanced by the ability to synchronize and/or by resilience to node failures. Previous work has shown that, in many cases, the largest eigenvalue of the network's adjacency matrix controls the onset of both synchronization and percolation processes. Thus, for networks whose functional performance is dependent on these processes, we propose a method that divides a given network into communities based on maximizing a function of the largest eigenvalues of the adjacency matrices of the resulting communities. We also explore the differences between the partitions obtained by our method and the modularity approach (which is based solely on consideration of network structure). We do this for several different classes of networks. We find that, in many cases, modularity-based partitions do almost as well as our function-based method in finding functional communities, even though modularity does not specifically incorporate consideration of function.

An Efficient Semi-fragile Watermarking Scheme for Tamper Localization and Recovery

NASA Astrophysics Data System (ADS)

Hou, Xiang; Yang, Hui; Min, Lianquan

2018-03-01

To solve the problem that remote sensing images are vulnerable to be tampered, a semi-fragile watermarking scheme was proposed. Binary random matrix was used as the authentication watermark, which was embedded by quantizing the maximum absolute value of directional sub-bands coefficients. The average gray level of every non-overlapping 4×4 block was adopted as the recovery watermark, which was embedded in the least significant bit. Watermarking detection could be done directly without resorting to the original images. Experimental results showed our method was robust against rational distortions to a certain extent. At the same time, it was fragile to malicious manipulation, and realized accurate localization and approximate recovery of the tampered regions. Therefore, this scheme can protect the security of remote sensing image effectively.

Using Social Network Analysis to Evaluate Community Capacity Building of a Regional Community Cancer Network

ERIC Educational Resources Information Center

Luque, John; Tyson, Dinorah Martinez; Lee, Ji-Hyun; Gwede, Clement; Vadaparampil, Susan; Noel-Thomas, Shalewa; Meade, Cathy

2010-01-01

The Tampa Bay Community Cancer Network (TBCCN) is one of 25 Community Network Programs funded by the National Cancer Institute's (NCI's) Center to Reduce Cancer Health Disparities with the objectives to create a collaborative infrastructure of academic and community based organizations and to develop effective and sustainable interventions to…

Internet pseudoscience: Testing opioid containing formulations with tampering potential.

PubMed

Pascali, Jennifer P; Fais, Paolo; Vaiano, Fabio; Pigaiani, Nicola; D'Errico, Stefano; Furlanetto, Sandra; Palumbo, Diego; Bertol, Elisabetta

2018-05-10

Drug tampering practices, with the aim to increase availability of drug delivery and/or enhance drug effects, are accessible on Internet and are practiced by some portion of recreational drug users. Not rarely, recreational misuse may result in toxic and even fatal results. The aim of the present study was to assess the tampering risk of medicaments containing different formulations of an opioid in combination with paracetamol or dexketoprofen, following the procedures reported in dedicated forums on the web. Tablets and suppositories containing codeine, tramadol and oxycodone were extracted following the reported "Cold water extraction"; dextromethorphan was extracted from cough syrup following the procedure reported as "Acid/base extraction" and fentanyl was extracted from transdermal patches according the procedure reported in Internet. The tampered products and opportunely prepared calibrators in water were analysed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). The separation of the analytes was carried on Agilent ZORBAX Eclipse Plus C18 (RRHT 2.1 mm × 50 mm, 1.8 μm) by the gradient elution of 0.01% formic acid in water and 0.01% formic acid in methanol. Acquisition was by MRM mode considering at least two transitions for compound. Declared recoveries for these home-made extractions claimed to exceed 99% for the opioid and to complete remove paracetamol, often associated to liver toxicity and thus to obtain a "safer" preparation. In this study, the authors demonstrated that rarely the recoveries for the opioid reached 90% and that up to 60% of the paracetamol amount remained in solution. Thus, high risks for health remained both for the potential lethality of the opioid content, but also for the sub-lethal chronic use of these mixtures, which contained still uncontrolled, ignored, but often important amounts of paracetamol. Copyright © 2018 Elsevier B.V. All rights reserved.

Finding overlapping communities in multilayer networks

PubMed Central

Liu, Weiyi; Suzumura, Toyotaro; Ji, Hongyu; Hu, Guangmin

2018-01-01

Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks. PMID:29694387

Knowledge Searching and Sharing on Virtual Networks.

ERIC Educational Resources Information Center

Helokunnas, Tuija; Herrala, Juha

2001-01-01

Describes searching and sharing of knowledge on virtual networks, based on experiences gained when hosting virtual knowledge networks at Tampere University of Technology in Finland. Discusses information and knowledge management studies; role of information technology in knowledge searching and sharing; implementation and experiences of the…

Efficiency of extraction and conversion of pseudoephedrine to methamphetamine from tamper-resistant and non-tamper-resistant formulations.

PubMed

Presley, Brandon; Bianchi, Bob; Coleman, John; Diamond, Fran; McNally, Gerry

2018-07-15

Clandestine chemists have demonstrated an ability to convert commercially available pseudoephedrine formulations to methamphetamine. Some of these formulations have properties that manufacturers claim limit or block the extraction of pseudoephedrine and its direct conversion to methamphetamine. In this study, 3 commercially available pseudoephedrine formulations were evaluated for ease of extraction and conversion to methamphetamine using a common chemistry technique called the one-pot method that is frequently employed by clandestine chemists. Two marketed pseudoephedrine formulations with claimed tamper-resistant properties - Zephrex-D ® and Nexafed ® - were compared to Sunmark ® , a comparator formulation of pseudoephedrine without tamper-resistant properties. Particle size reduction was conducted using 8 readily available tools; solubility was assessed using 2 common aqueous solutions and various reaction conditions (e.g., temperature, stirring); extractability was evaluated using 8 common organic solvents. The one-pot (single vessel) method commonly used in clandestine processes was employed; chemicals and equipment were purchased locally on the open market. Quantities and addition times of the chemicals used to carry out the procedure and the duration of the reaction were varied to determine the effect on methamphetamine yield. The procedure was appropriately scaled and conducted in a controlled environment to reduce risk and maximize yields. Pseudoephedrine and methamphetamine were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Standard quantitative procedures were used to determine the quantities of pseudoephedrine and methamphetamine recovered and produced, respectively. Particle size reduction resulted in some loss of material of each pseudoephedrine formulation; Zephrex-D tablets were broken down to a coarse material; Nexafed and Sunmark tablets were reduced to a fine powder. The solubility rates of intact and ground

Epidemic spreading on complex networks with community structures

PubMed Central

Stegehuis, Clara; van der Hofstad, Remco; van Leeuwaarden, Johan S. H.

2016-01-01

Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, while the other model only preserves the set of communities and the vertex degrees. These models show that community structure is an important determinant of the behavior of percolation processes on networks, such as information diffusion or virus spreading: the community structure can both enforce as well as inhibit diffusion processes. Our models further show that it is the mesoscopic set of communities that matters. The exact internal structures of communities barely influence the behavior of percolation processes across networks. This insensitivity is likely due to the relative denseness of the communities. PMID:27440176

Efficient discovery of overlapping communities in massive networks

PubMed Central

Gopalan, Prem K.; Blei, David M.

2013-01-01

Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks. PMID:23950224

Overlapping Community Detection based on Network Decomposition

NASA Astrophysics Data System (ADS)

Ding, Zhuanlian; Zhang, Xingyi; Sun, Dengdi; Luo, Bin

2016-04-01

Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent drawbacks to discover overlapping communities. Node clustering is inadequate to capture the pervasive overlaps, while link clustering is often criticized due to the high computational cost and ambiguous definition of communities. So, overlapping community detection is still a formidable challenge. In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD. Specifically, NDOCD iteratively splits the network by removing all links in derived link communities, which are identified by utilizing node clustering technique. The network decomposition contributes to reducing the computation time and noise link elimination conduces to improving the quality of obtained communities. Besides, we employ node clustering technique rather than link similarity measure to discover link communities, thus NDOCD avoids an ambiguous definition of community and becomes less time-consuming. We test our approach on both synthetic and real-world networks. Results demonstrate the superior performance of our approach both in computation time and accuracy compared to state-of-the-art algorithms.

Decoding communities in networks

NASA Astrophysics Data System (ADS)

Radicchi, Filippo

2018-02-01

According to a recent information-theoretical proposal, the problem of defining and identifying communities in networks can be interpreted as a classical communication task over a noisy channel: memberships of nodes are information bits erased by the channel, edges and nonedges in the network are parity bits introduced by the encoder but degraded through the channel, and a community identification algorithm is a decoder. The interpretation is perfectly equivalent to the one at the basis of well-known statistical inference algorithms for community detection. The only difference in the interpretation is that a noisy channel replaces a stochastic network model. However, the different perspective gives the opportunity to take advantage of the rich set of tools of coding theory to generate novel insights on the problem of community detection. In this paper, we illustrate two main applications of standard coding-theoretical methods to community detection. First, we leverage a state-of-the-art decoding technique to generate a family of quasioptimal community detection algorithms. Second and more important, we show that the Shannon's noisy-channel coding theorem can be invoked to establish a lower bound, here named as decodability bound, for the maximum amount of noise tolerable by an ideal decoder to achieve perfect detection of communities. When computed for well-established synthetic benchmarks, the decodability bound explains accurately the performance achieved by the best community detection algorithms existing on the market, telling us that only little room for their improvement is still potentially left.

Decoding communities in networks.

PubMed

Radicchi, Filippo

2018-02-01

According to a recent information-theoretical proposal, the problem of defining and identifying communities in networks can be interpreted as a classical communication task over a noisy channel: memberships of nodes are information bits erased by the channel, edges and nonedges in the network are parity bits introduced by the encoder but degraded through the channel, and a community identification algorithm is a decoder. The interpretation is perfectly equivalent to the one at the basis of well-known statistical inference algorithms for community detection. The only difference in the interpretation is that a noisy channel replaces a stochastic network model. However, the different perspective gives the opportunity to take advantage of the rich set of tools of coding theory to generate novel insights on the problem of community detection. In this paper, we illustrate two main applications of standard coding-theoretical methods to community detection. First, we leverage a state-of-the-art decoding technique to generate a family of quasioptimal community detection algorithms. Second and more important, we show that the Shannon's noisy-channel coding theorem can be invoked to establish a lower bound, here named as decodability bound, for the maximum amount of noise tolerable by an ideal decoder to achieve perfect detection of communities. When computed for well-established synthetic benchmarks, the decodability bound explains accurately the performance achieved by the best community detection algorithms existing on the market, telling us that only little room for their improvement is still potentially left.

Community networks in chronic disease management.

PubMed

Pyne, Diane

2009-01-01

Community networks are being established as part of the Chronic Disease Management program in Edmonton, Alberta. These networks are programs and services from profit and not-for-profit organizations that support people with chronic conditions to address lifestyle choices and issues. Evidence-informed standards and criteria have been developed that have to be met to belong to such a network. The community network approach is developing a "community" of resources that are available and committed to assist healthcare professionals and the public with health promotion for people with chronic conditions.

Constant Communities in Complex Networks

NASA Astrophysics Data System (ADS)

Chakraborty, Tanmoy; Srinivasan, Sriram; Ganguly, Niloy; Bhowmick, Sanjukta; Mukherjee, Animesh

2013-05-01

Identifying community structure is a fundamental problem in network analysis. Most community detection algorithms are based on optimizing a combinatorial parameter, for example modularity. This optimization is generally NP-hard, thus merely changing the vertex order can alter their assignments to the community. However, there has been less study on how vertex ordering influences the results of the community detection algorithms. Here we identify and study the properties of invariant groups of vertices (constant communities) whose assignment to communities are, quite remarkably, not affected by vertex ordering. The percentage of constant communities can vary across different applications and based on empirical results we propose metrics to evaluate these communities. Using constant communities as a pre-processing step, one can significantly reduce the variation of the results. Finally, we present a case study on phoneme network and illustrate that constant communities, quite strikingly, form the core functional units of the larger communities.

21 CFR 700.25 - Tamper-resistant packaging requirements for cosmetic products.

Code of Federal Regulations, 2014 CFR

2014-04-01

... cosmetic products. 700.25 Section 700.25 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) COSMETICS GENERAL Requirements for Specific Cosmetic Products § 700.25 Tamper-resistant packaging requirements for cosmetic products. (a) General. Because most cosmetic liquid...

21 CFR 700.25 - Tamper-resistant packaging requirements for cosmetic products.

Code of Federal Regulations, 2013 CFR

2013-04-01

... cosmetic products. 700.25 Section 700.25 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) COSMETICS GENERAL Requirements for Specific Cosmetic Products § 700.25 Tamper-resistant packaging requirements for cosmetic products. (a) General. Because most cosmetic liquid...

21 CFR 700.25 - Tamper-resistant packaging requirements for cosmetic products.

Code of Federal Regulations, 2012 CFR

2012-04-01

... cosmetic products. 700.25 Section 700.25 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) COSMETICS GENERAL Requirements for Specific Cosmetic Products § 700.25 Tamper-resistant packaging requirements for cosmetic products. (a) General. Because most cosmetic liquid...

21 CFR 700.25 - Tamper-resistant packaging requirements for cosmetic products.

Code of Federal Regulations, 2011 CFR

2011-04-01

... cosmetic products. 700.25 Section 700.25 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) COSMETICS GENERAL Requirements for Specific Cosmetic Products § 700.25 Tamper-resistant packaging requirements for cosmetic products. (a) General. Because most cosmetic liquid...

Effects of multiple spreaders in community networks

NASA Astrophysics Data System (ADS)

Hu, Zhao-Long; Ren, Zhuo-Ming; Yang, Guang-Yong; Liu, Jian-Guo

2014-12-01

Human contact networks exhibit the community structure. Understanding how such community structure affects the epidemic spreading could provide insights for preventing the spreading of epidemics between communities. In this paper, we explore the spreading of multiple spreaders in community networks. A network based on the clustering preferential mechanism is evolved, whose communities are detected by the Girvan-Newman (GN) algorithm. We investigate the spreading effectiveness by selecting the nodes as spreaders in the following ways: nodes with the largest degree in each community (community hubs), the same number of nodes with the largest degree from the global network (global large-degree) and randomly selected one node within each community (community random). The experimental results on the SIR model show that the spreading effectiveness based on the global large-degree and community hubs methods is the same in the early stage of the infection and the method of community random is the worst. However, when the infection rate exceeds the critical value, the global large-degree method embodies the worst spreading effectiveness. Furthermore, the discrepancy of effectiveness for the three methods will decrease as the infection rate increases. Therefore, we should immunize the hubs in each community rather than those hubs in the global network to prevent the outbreak of epidemics.

Maximal Neighbor Similarity Reveals Real Communities in Networks

PubMed Central

Žalik, Krista Rizman

2015-01-01

An important problem in the analysis of network data is the detection of groups of densely interconnected nodes also called modules or communities. Community structure reveals functions and organizations of networks. Currently used algorithms for community detection in large-scale real-world networks are computationally expensive or require a priori information such as the number or sizes of communities or are not able to give the same resulting partition in multiple runs. In this paper we investigate a simple and fast algorithm that uses the network structure alone and requires neither optimization of pre-defined objective function nor information about number of communities. We propose a bottom up community detection algorithm in which starting from communities consisting of adjacent pairs of nodes and their maximal similar neighbors we find real communities. We show that the overall advantage of the proposed algorithm compared to the other community detection algorithms is its simple nature, low computational cost and its very high accuracy in detection communities of different sizes also in networks with blurred modularity structure consisting of poorly separated communities. All communities identified by the proposed method for facebook network and E-Coli transcriptional regulatory network have strong structural and functional coherence. PMID:26680448

Network communities within and across borders.

PubMed

Cerina, Federica; Chessa, Alessandro; Pammolli, Fabio; Riccaboni, Massimo

2014-04-01

We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technological policy-making, highlighting the interplay between pressure for the internationalization to lead towards a global innovation system and the administrative borders imposed by the national and regional institutions. In this study we introduce an outreach index to quantify the impact of borders on the community structure and apply it to the case of the European and US patent co-inventors networks. We find that (a) the US connectivity decays as a power of distance, whereas we observe a faster exponential decay for Europe; (b) European network communities essentially correspond to nations and contiguous regions while US communities span multiple states across the whole country without any characteristic geographic scale. We confirm our findings by means of a set of simulations aimed at exploring the relationship between different patterns of cross-border community structures and the outreach index.

Social networks and community prevention coalitions.

PubMed

Feinberg, Mark E; Riggs, Nathaniel R; Greenberg, Mark T

2005-07-01

This study investigates the links between community readiness and the social networks among participants in Communities That Care (CTC), community-based prevention coalitions. The coalitions targeted adolescent behavior problems through community risk factor assessments, prioritization of risk factors, and selection/implementation of corresponding evidence-based family, school, and community programs. Key leaders (n = 219) in 23 new CTC sites completed questionnaires focusing on community readiness to implement CTC and the respondents' personal, work, and social organization links to other key leaders in the community. Outside technical assistants also completed ratings of each community's readiness and early CTC functioning. Measures of network cohesion/integration were positively associated with readiness, while centralization was negatively associated. These results suggest that non-centralized networks in which ties between members are close and direct may be an indicator of community readiness. In addition, we found different associations between readiness and different domains of social relations. EDITORS' STRATEGIC IMPLICATIONS: The authors present the promising practice of using social network analysis to characterize the functioning of local prevention coalitions and their readiness to implement a community-based prevention initiative. Researchers and community planners will benefit from the lessons in this article, which capitalizes on a large sample and multiple informants. This work raises interesting questions about how to combine the promotion of coalition functioning while simultaneously encouraging diversity of coalition membership.

Changes in the dispensing of opioid medications in Canada following the introduction of a tamper-deterrent formulation of long-acting oxycodone: a time series analysis.

PubMed

Gomes, Tara; Mastorakos, Andrea; Paterson, J Michael; Sketris, Ingrid; Caetano, Patricia; Greaves, Simon; Henry, David

2017-11-22

In February 2012, a reformulated tamper-deterrent form of long-acting oxycodone, OxyNeo, was introduced in Canada. We investigated the impact of the introduction of OxyNeo on patterns of opioid prescribing. We conducted population-based, cross-sectional analyses of opioid dispensing in Canada between 2008 and 2016. We estimated monthly community pharmacy dispensing of oral formulations of codeine, morphine, hydromorphone and oxycodone, and a transdermal formulation of fentanyl, and converted quantities to milligrams of morphine equivalents (MMEs) per 1000 population. We used time series analysis to evaluate the effect of the introduction of OxyNeo on these trends. National dispensing of long-acting opioids fell by 14.9% between February 2012 and April 2016, from 36 098 MMEs to 30 716 MMEs per 1000 population ( p < 0.01). This effect varied across Canada and was largest in Ontario (reduction of 22.8%) ( p = 0.01) and British Columbia (reduction of 30.0%) ( p = 0.01). The national rate of oxycodone dispensing fell by 46.4% after the introduction of OxyNeo ( p < 0.001); this was partially offset by an increase of 47.8% in hydromorphone dispensing ( p < 0.001). Although dispensing of immediate-release opioids was a substantial contributor to overall population opioid exposure across Canada, it was unaffected by the introduction of OxyNeo ( p > 0.05 in all provinces). The findings suggest that the introduction of a tamper-deterrent formulation of long-acting oxycodone in Canada, against a background of changing public drug benefits, was associated with sustained changes in selection of long-acting opioids but only small changes in the quantity of long-acting opioids dispensed. This illustrates the limited effect a tamper-deterrent formulation and associated coverage policy can have when other, non-tamper-deterrent alternatives are readily available. Copyright 2017, Joule Inc. or its licensors.

Finding Statistically Significant Communities in Networks

PubMed Central

Lancichinetti, Andrea; Radicchi, Filippo; Ramasco, José J.; Fortunato, Santo

2011-01-01

Community structure is one of the main structural features of networks, revealing both their internal organization and the similarity of their elementary units. Despite the large variety of methods proposed to detect communities in graphs, there is a big need for multi-purpose techniques, able to handle different types of datasets and the subtleties of community structure. In this paper we present OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics. It is based on the local optimization of a fitness function expressing the statistical significance of clusters with respect to random fluctuations, which is estimated with tools of Extreme and Order Statistics. OSLOM can be used alone or as a refinement procedure of partitions/covers delivered by other techniques. We have also implemented sequential algorithms combining OSLOM with other fast techniques, so that the community structure of very large networks can be uncovered. Our method has a comparable performance as the best existing algorithms on artificial benchmark graphs. Several applications on real networks are shown as well. OSLOM is implemented in a freely available software (http://www.oslom.org), and we believe it will be a valuable tool in the analysis of networks. PMID:21559480

Finding community structure in very large networks

NASA Astrophysics Data System (ADS)

Clauset, Aaron; Newman, M. E. J.; Moore, Cristopher

2004-12-01

The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(mdlogn) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with mtilde n and dtilde logn , in which case our algorithm runs in essentially linear time, O(nlog2n) . As an example of the application of this algorithm we use it to analyze a network of items for sale on the web site of a large on-line retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400 000 vertices and 2×106 edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.

21 CFR 700.25 - Tamper-resistant packaging requirements for cosmetic products.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 7 2010-04-01 2010-04-01 false Tamper-resistant packaging requirements for cosmetic products. 700.25 Section 700.25 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH... of cosmetic product packages. The Food and Drug Administration has the authority and responsibility...

Network communities within and across borders

PubMed Central

Cerina, Federica; Chessa, Alessandro; Pammolli, Fabio; Riccaboni, Massimo

2014-01-01

We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technological policy-making, highlighting the interplay between pressure for the internationalization to lead towards a global innovation system and the administrative borders imposed by the national and regional institutions. In this study we introduce an outreach index to quantify the impact of borders on the community structure and apply it to the case of the European and US patent co-inventors networks. We find that (a) the US connectivity decays as a power of distance, whereas we observe a faster exponential decay for Europe; (b) European network communities essentially correspond to nations and contiguous regions while US communities span multiple states across the whole country without any characteristic geographic scale. We confirm our findings by means of a set of simulations aimed at exploring the relationship between different patterns of cross-border community structures and the outreach index. PMID:24686380

Tamper Indicating Device: Initial Training, Course 50112

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bonner, Stephen Ray; Sandoval, Dana M.

Tamper Indicating Device (TID): Initial Training, course #50112, covers Los Alamos National Laboratory (LANL) Material Control & Accountability (MC&A) TID Program procedures for the application and removal of TIDs. LANL’s policy is to comply with Department of Energy (DOE) requirements for the use of TIDs consistent with the graded safeguards described in DOE Manual DOE O 474.2, Nuclear Material Control and Accountability. When you have completed this course, you will: recognize standard practices and procedures of the LANL TID Program; have hands-on experience in the application and removal of LANL TIDs, and; verify the application and removal of LANL TIDs.

Emergence of communities and diversity in social networks

PubMed Central

Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross

2017-01-01

Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics. PMID:28235785

Emergence of communities and diversity in social networks.

PubMed

Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross; Stanley, H Eugene

2017-03-14

Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics.

Developing community networks to deliver HIV prevention interventions.

PubMed Central

Guenther-Grey, C; Noroian, D; Fonseka, J; Higgins, D

1996-01-01

Outreach has a long history in health and social service programs as an important method for reaching at-risk persons within their communities. One method of "outreach" is based on the recruitment of networks of community members (or "networkers") to deliver HIV prevention messages and materials in the context of their social networks and everyday lives. This paper documents the experiences of the AIDS Community Demonstration Projects in recruiting networkers to deliver HIV prevention interventions to high-risk populations, including injecting drug users not in treatment; female sex partners of injecting drug users; female sex traders; men who have sex with men but do not self-identify as gay; and youth in high-risk situations. The authors interviewed project staff and reviewed project records of the implementation of community networks in five cities. Across cities, the projects successfully recruited persons into one or more community networks to distribute small media materials, condoms, and bleach kits, and encourage risk-reduction behaviors among community members. Networkers' continuing participation was enlisted through a variety of monetary and nonmonetary incentives. While continuous recruitment of networkers was necessary due to attrition, most interventions reported maintaining a core group of networkers. In addition, the projects appeared to serve as a starting point for some networkers to become more active in other community events and issues. PMID:8862156

Towards Online Multiresolution Community Detection in Large-Scale Networks

PubMed Central

Huang, Jianbin; Sun, Heli; Liu, Yaguang; Song, Qinbao; Weninger, Tim

2011-01-01

The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks. PMID:21887325

Community Attachment and Satisfaction: The Role of a Community's Social Network Structure

ERIC Educational Resources Information Center

Crowe, Jessica

2010-01-01

This paper links the micro and macro levels of analysis by examining how different aspects of community sentiment are affected by one's personal ties to the community compared with the organizational network structure of the community. Using data collected from residents of six communities in Washington State, network analysis combined with…

The community structure of the global corporate network.

PubMed

Vitali, Stefania; Battiston, Stefano

2014-01-01

We investigate the community structure of the global ownership network of transnational corporations. We find a pronounced organization in communities that cannot be explained by randomness. Despite the global character of this network, communities reflect first of all the geographical location of firms, while the industrial sector plays only a marginal role. We also analyze the meta-network in which the nodes are the communities and the links are obtained by aggregating the links among firms belonging to pairs of communities. We analyze the network centrality of the top 50 communities and we provide a quantitative assessment of the financial sector role in connecting the global economy.

The Community Structure of the Global Corporate Network

PubMed Central

Vitali, Stefania; Battiston, Stefano

2014-01-01

We investigate the community structure of the global ownership network of transnational corporations. We find a pronounced organization in communities that cannot be explained by randomness. Despite the global character of this network, communities reflect first of all the geographical location of firms, while the industrial sector plays only a marginal role. We also analyze the meta-network in which the nodes are the communities and the links are obtained by aggregating the links among firms belonging to pairs of communities. We analyze the network centrality of the top 50 communities and we provide a quantitative assessment of the financial sector role in connecting the global economy. PMID:25126722

Multiway spectral community detection in networks

NASA Astrophysics Data System (ADS)

Zhang, Xiao; Newman, M. E. J.

2015-11-01

One of the most widely used methods for community detection in networks is the maximization of the quality function known as modularity. Of the many maximization techniques that have been used in this context, some of the most conceptually attractive are the spectral methods, which are based on the eigenvectors of the modularity matrix. Spectral algorithms have, however, been limited, by and large, to the division of networks into only two or three communities, with divisions into more than three being achieved by repeated two-way division. Here we present a spectral algorithm that can directly divide a network into any number of communities. The algorithm makes use of a mapping from modularity maximization to a vector partitioning problem, combined with a fast heuristic for vector partitioning. We compare the performance of this spectral algorithm with previous approaches and find it to give superior results, particularly in cases where community sizes are unbalanced. We also give demonstrative applications of the algorithm to two real-world networks and find that it produces results in good agreement with expectations for the networks studied.

A generalised significance test for individual communities in networks.

PubMed

Kojaku, Sadamori; Masuda, Naoki

2018-05-09

Many empirical networks have community structure, in which nodes are densely interconnected within each community (i.e., a group of nodes) and sparsely across different communities. Like other local and meso-scale structure of networks, communities are generally heterogeneous in various aspects such as the size, density of edges, connectivity to other communities and significance. In the present study, we propose a method to statistically test the significance of individual communities in a given network. Compared to the previous methods, the present algorithm is unique in that it accepts different community-detection algorithms and the corresponding quality function for single communities. The present method requires that a quality of each community can be quantified and that community detection is performed as optimisation of such a quality function summed over the communities. Various community detection algorithms including modularity maximisation and graph partitioning meet this criterion. Our method estimates a distribution of the quality function for randomised networks to calculate a likelihood of each community in the given network. We illustrate our algorithm by synthetic and empirical networks.

Cascading failures in complex networks with community structure

NASA Astrophysics Data System (ADS)

Lin, Guoqiang; di, Zengru; Fan, Ying

2014-12-01

Much empirical evidence shows that when attacked with cascading failures, scale-free or even random networks tend to collapse more extensively when the initially deleted node has higher betweenness. Meanwhile, in networks with strong community structure, high-betweenness nodes tend to be bridge nodes that link different communities, and the removal of such nodes will reduce only the connections among communities, leaving the networks fairly stable. Understanding what will affect cascading failures and how to protect or attack networks with strong community structure is therefore of interest. In this paper, we have constructed scale-free Community Networks (SFCN) and Random Community Networks (RCN). We applied these networks, along with the Lancichinett-Fortunato-Radicchi (LFR) benchmark, to the cascading-failure scenario to explore their vulnerability to attack and the relationship between cascading failures and the degree distribution and community structure of a network. The numerical results show that when the networks are of a power-law distribution, a stronger community structure will result in the failure of fewer nodes. In addition, the initial removal of the node with the highest betweenness will not lead to the worst cascading, i.e. the largest avalanche size. The Betweenness Overflow (BOF), an index that we developed, is an effective indicator of this tendency. The RCN, however, display a different result. In addition, the avalanche size of each node can be adopted as an index to evaluate the importance of the node.

Networked Community Change: Understanding Community Systems Change through the Lens of Social Network Analysis.

PubMed

Lawlor, Jennifer A; Neal, Zachary P

2016-06-01

Addressing complex problems in communities has become a key area of focus in recent years (Kania & Kramer, 2013, Stanford Social Innovation Review). Building on existing approaches to understanding and addressing problems, such as action research, several new approaches have emerged that shift the way communities solve problems (e.g., Burns, 2007, Systemic Action Research; Foth, 2006, Action Research, 4, 205; Kania & Kramer, 2011, Stanford Social Innovation Review, 1, 36). Seeking to bring clarity to the emerging literature on community change strategies, this article identifies the common features of the most widespread community change strategies and explores the conditions under which such strategies have the potential to be effective. We identify and describe five common features among the approaches to change. Then, using an agent-based model, we simulate network-building behavior among stakeholders participating in community change efforts using these approaches. We find that the emergent stakeholder networks are efficient when the processes are implemented under ideal conditions. © Society for Community Research and Action 2016.

Netgram: Visualizing Communities in Evolving Networks

PubMed Central

Mall, Raghvendra; Langone, Rocco; Suykens, Johan A. K.

2015-01-01

Real-world complex networks are dynamic in nature and change over time. The change is usually observed in the interactions within the network over time. Complex networks exhibit community like structures. A key feature of the dynamics of complex networks is the evolution of communities over time. Several methods have been proposed to detect and track the evolution of these groups over time. However, there is no generic tool which visualizes all the aspects of group evolution in dynamic networks including birth, death, splitting, merging, expansion, shrinkage and continuation of groups. In this paper, we propose Netgram: a tool for visualizing evolution of communities in time-evolving graphs. Netgram maintains evolution of communities over 2 consecutive time-stamps in tables which are used to create a query database using the sql outer-join operation. It uses a line-based visualization technique which adheres to certain design principles and aesthetic guidelines. Netgram uses a greedy solution to order the initial community information provided by the evolutionary clustering technique such that we have fewer line cross-overs in the visualization. This makes it easier to track the progress of individual communities in time evolving graphs. Netgram is a generic toolkit which can be used with any evolutionary community detection algorithm as illustrated in our experiments. We use Netgram for visualization of topic evolution in the NIPS conference over a period of 11 years and observe the emergence and merging of several disciplines in the field of information processing systems. PMID:26356538

A novel community detection method in bipartite networks

NASA Astrophysics Data System (ADS)

Zhou, Cangqi; Feng, Liang; Zhao, Qianchuan

2018-02-01

Community structure is a common and important feature in many complex networks, including bipartite networks, which are used as a standard model for many empirical networks comprised of two types of nodes. In this paper, we propose a two-stage method for detecting community structure in bipartite networks. Firstly, we extend the widely-used Louvain algorithm to bipartite networks. The effectiveness and efficiency of the Louvain algorithm have been proved by many applications. However, there lacks a Louvain-like algorithm specially modified for bipartite networks. Based on bipartite modularity, a measure that extends unipartite modularity and that quantifies the strength of partitions in bipartite networks, we fill the gap by developing the Bi-Louvain algorithm that iteratively groups the nodes in each part by turns. This algorithm in bipartite networks often produces a balanced network structure with equal numbers of two types of nodes. Secondly, for the balanced network yielded by the first algorithm, we use an agglomerative clustering method to further cluster the network. We demonstrate that the calculation of the gain of modularity of each aggregation, and the operation of joining two communities can be compactly calculated by matrix operations for all pairs of communities simultaneously. At last, a complete hierarchical community structure is unfolded. We apply our method to two benchmark data sets and a large-scale data set from an e-commerce company, showing that it effectively identifies community structure in bipartite networks.

Cooperation in the prisoner's dilemma game on tunable community networks

NASA Astrophysics Data System (ADS)

Liu, Penghui; Liu, Jing

2017-04-01

Community networks have attracted lots of attention as they widely exist in the real world and are essential to study properties of networks. As the game theory illustrates the competitive relationship among individuals, studying the iterated prisoner's dilemma games (PDG) on community networks is meaningful. In this paper, we focus on investigating the relationship between the cooperation level of community networks and that of their communities in the prisoner's dilemma games. With this purpose in mind, a type of tunable community networks whose communities inherit not only the scale-free property, but also the characteristic of adjustable cooperation level of Holme and Kim (HK) networks is designed. Both uniform and non-uniform community networks are investigated. We find out that cooperation enhancement of communities can improve the cooperation level of the whole networks. Moreover, simulation results indicate that a large community is a better choice than a small community to improve the cooperation level of the whole networks. Thus, improving the cooperation level of community networks can be divided into a number of sub-problems targeting at improving the cooperation level of individual communities, which can save the computation cost and deal with the problem of improving the cooperation level of huge community networks. Moreover, as the larger community is a better choice, it is reasonable to start with large communities, according to the greedy strategy when the number of nodes can participate in the enhancement is limited.

Reusable, tamper-indicating seal

DOEpatents

Ryan, Michael J.

1978-01-01

A reusable, tamper-indicating seal comprises a drum confined within a fixed body and rotatable in one direction therewithin, the top of the drum constituting a tray carrying a large number of small balls of several different colors. The fixed body contains parallel holes for looping a seal wire therethrough. The base of the drums carries cams adapted to coact with cam followers to lock the wire within the seal at one angular position of the drum. A channel in the fixed body -- visible from outside the seal -- adjacent the tray constitutes a segregated location for a small plurality of the colored balls. A spring in the tray forces colored balls into the segregated location at one angular position of the drum, further rotation securing the balls in position and the wires in the seal. A wedge-shaped plough removes the balls from the segregated location, at a different angular position of the drum, the wire being unlocked at the same position. A new pattern of colored balls will appear in the segregated location when the seal is relocked.

Optimal multi-community network modularity for information diffusion

NASA Astrophysics Data System (ADS)

Wu, Jiaocan; Du, Ruping; Zheng, Yingying; Liu, Dong

2016-02-01

Studies demonstrate that community structure plays an important role in information spreading recently. In this paper, we investigate the impact of multi-community structure on information diffusion with linear threshold model. We utilize extended GN network that contains four communities and analyze dynamic behaviors of information that spreads on it. And we discover the optimal multi-community network modularity for information diffusion based on the social reinforcement. Results show that, within the appropriate range, multi-community structure will facilitate information diffusion instead of hindering it, which accords with the results derived from two-community network.

Brand communities embedded in social networks.

PubMed

Zaglia, Melanie E

2013-02-01

Brand communities represent highly valuable marketing, innovation management, and customer relationship management tools. However, applying successful marketing strategies today, and in the future, also means exploring and seizing the unprecedented opportunities of social network environments. This study combines these two social phenomena which have largely been researched separately, and aims to investigate the existence, functionality and different types of brand communities within social networks. The netnographic approach yields strong evidence of this existence; leading to a better understanding of such embedded brand communities, their peculiarities, and motivational drivers for participation; therefore the findings contribute to theory by combining two separate research streams. Due to the advantages of social networks, brand management is now able to implement brand communities with less time and financial effort; however, choosing the appropriate brand community type, cultivating consumers' interaction, and staying tuned to this social engagement are critical factors to gain anticipated brand outcomes.

Wireless tamper detection sensor and sensing system

NASA Technical Reports Server (NTRS)

Woodard, Stanley E. (Inventor); Taylor, Bryant D. (Inventor)

2011-01-01

A wireless tamper detection sensor is defined by a perforated electrical conductor. The conductor is shaped to form a geometric pattern between first and second ends thereof such that the conductor defines an open-circuit that can store and transfer electrical and magnetic energy. The conductor resonates in the presence of a time-varying magnetic field to generate a harmonic response. The harmonic response changes when the conductor experiences a change in its geometric pattern due to severing of the conductor along at least a portion of the perforations. A magnetic field response recorder is used to wirelessly transmit the time-varying magnetic field and wirelessly detecting the conductor's harmonic response.

Network Analysis in Community Psychology: Looking Back, Looking Forward.

PubMed

Neal, Zachary P; Neal, Jennifer Watling

2017-09-01

Network analysis holds promise for community psychology given the field's aim to understand the interplay between individuals and their social contexts. Indeed, because network analysis focuses explicitly on patterns of relationships between actors, its theories and methods are inherently extra-individual in nature and particularly well suited to characterizing social contexts. But, to what extent has community psychology taken advantage of this network analysis as a tool for capturing context? To answer these questions, this study provides a review of the use network analysis in articles published in American Journal of Community Psychology. Looking back, we describe and summarize the ways that network analysis has been employed in community psychology research to understand the range of ways community psychologists have found the technique helpful. Looking forward and paying particular attention to analytic issues identified in past applications, we provide some recommendations drawn from the network analysis literature to facilitate future applications of network analysis in community psychology. © 2017 The Authors. American Journal of Community Psychology published by Wiley Periodicals, Inc. on behalf of Society for Community Research and Action.

Community Detection in Complex Networks via Clique Conductance.

PubMed

Lu, Zhenqi; Wahlström, Johan; Nehorai, Arye

2018-04-13

Network science plays a central role in understanding and modeling complex systems in many areas including physics, sociology, biology, computer science, economics, politics, and neuroscience. One of the most important features of networks is community structure, i.e., clustering of nodes that are locally densely interconnected. Communities reveal the hierarchical organization of nodes, and detecting communities is of great importance in the study of complex systems. Most existing community-detection methods consider low-order connection patterns at the level of individual links. But high-order connection patterns, at the level of small subnetworks, are generally not considered. In this paper, we develop a novel community-detection method based on cliques, i.e., local complete subnetworks. The proposed method overcomes the deficiencies of previous similar community-detection methods by considering the mathematical properties of cliques. We apply the proposed method to computer-generated graphs and real-world network datasets. When applied to networks with known community structure, the proposed method detects the structure with high fidelity and sensitivity. When applied to networks with no a priori information regarding community structure, the proposed method yields insightful results revealing the organization of these complex networks. We also show that the proposed method is guaranteed to detect near-optimal clusters in the bipartition case.

Community Evolution in International Migration Top1 Networks.

PubMed

Peres, Mihaela; Xu, Helian; Wu, Gang

2016-01-01

Focusing on each country's topmost destination/origin migration relation with other countries, this study builds top1 destination networks and top1 origin networks in order to understand their skeletal construction and community dynamics. Each top1 network covers approximately 50% of the complete migrant network stock for each decade between 1960 and 2000. We investigate the community structure by implementing the Girvan-Newman algorithm and compare the number of components and communities to illustrate their differences. We find that (i) both top1 networks (origin and destination) exhibited communities with a clear structure and a surprising evolution, although 80% edges persist between each decade; (ii) top1 destination networks focused on developed countries exhibiting shorter paths and preferring more advance countries, while top1 origin networks focused both on developed as well as more substantial developing nations that presented a longer path and more stable groups; (iii) only few countries have a decisive influence on community evolution of both top1 networks. USA took the leading position as a destination country in top1 destination networks, while China and India were the main Asian emigration countries in top1 origin networks; European countries and the Russian Federation played an important role in both.

Community Evolution in International Migration Top1 Networks

PubMed Central

Xu, Helian

2016-01-01

Focusing on each country’s topmost destination/origin migration relation with other countries, this study builds top1 destination networks and top1 origin networks in order to understand their skeletal construction and community dynamics. Each top1 network covers approximately 50% of the complete migrant network stock for each decade between 1960 and 2000. We investigate the community structure by implementing the Girvan-Newman algorithm and compare the number of components and communities to illustrate their differences. We find that (i) both top1 networks (origin and destination) exhibited communities with a clear structure and a surprising evolution, although 80% edges persist between each decade; (ii) top1 destination networks focused on developed countries exhibiting shorter paths and preferring more advance countries, while top1 origin networks focused both on developed as well as more substantial developing nations that presented a longer path and more stable groups; (iii) only few countries have a decisive influence on community evolution of both top1 networks. USA took the leading position as a destination country in top1 destination networks, while China and India were the main Asian emigration countries in top1 origin networks; European countries and the Russian Federation played an important role in both. PMID:26859406

The ADVANCE network: accelerating data value across a national community health center network

PubMed Central

DeVoe, Jennifer E; Gold, Rachel; Cottrell, Erika; Bauer, Vance; Brickman, Andrew; Puro, Jon; Nelson, Christine; Mayer, Kenneth H; Sears, Abigail; Burdick, Tim; Merrell, Jonathan; Matthews, Paul; Fields, Scott

2014-01-01

The ADVANCE (Accelerating Data Value Across a National Community Health Center Network) clinical data research network (CDRN) is led by the OCHIN Community Health Information Network in partnership with Health Choice Network and Fenway Health. The ADVANCE CDRN will ‘horizontally’ integrate outpatient electronic health record data for over one million federally qualified health center patients, and ‘vertically’ integrate hospital, health plan, and community data for these patients, often under-represented in research studies. Patient investigators, community investigators, and academic investigators with diverse expertise will work together to meet project goals related to data integration, patient engagement and recruitment, and the development of streamlined regulatory policies. By enhancing the data and research infrastructure of participating organizations, the ADVANCE CDRN will serve as a ‘community laboratory’ for including disadvantaged and vulnerable patients in patient-centered outcomes research that is aligned with the priorities of patients, clinics, and communities in our network. PMID:24821740

Epidemics in adaptive networks with community structure

NASA Astrophysics Data System (ADS)

Shaw, Leah; Tunc, Ilker

2010-03-01

Models for epidemic spread on static social networks do not account for changes in individuals' social interactions. Recent studies of adaptive networks have modeled avoidance behavior, as non-infected individuals try to avoid contact with infectives. Such models have not generally included realistic social structure. Here we study epidemic spread on an adaptive network with community structure. We model the effect of heterogeneous communities on infection levels and epidemic extinction. We also show how an epidemic can alter the community structure.

Homophyly/kinship hypothesis: Natural communities, and predicting in networks

NASA Astrophysics Data System (ADS)

Li, Angsheng; Li, Jiankou; Pan, Yicheng

2015-02-01

It has been a longstanding challenge to understand natural communities in real world networks. We proposed a community finding algorithm based on fitness of networks, two algorithms for prediction, accurate prediction and confirmation of keywords for papers in the citation network Arxiv HEP-TH (high energy physics theory), and the measures of internal centrality, external de-centrality, internal and external slopes to characterize the structures of communities. We implemented our algorithms on 2 citation and 5 cooperation graphs. Our experiments explored and validated a homophyly/kinship principle of real world networks. The homophyly/kinship principle includes: (1) homophyly is the natural selection in real world networks, similar to Darwin's kinship selection in nature, (2) real world networks consist of natural communities generated by the natural selection of homophyly, (3) most individuals in a natural community share a short list of common attributes, (4) natural communities have an internal centrality (or internal heterogeneity) that a natural community has a few nodes dominating most of the individuals in the community, (5) natural communities have an external de-centrality (or external homogeneity) that external links of a natural community homogeneously distributed in different communities, and (6) natural communities of a given network have typical structures determined by the internal slopes, and have typical patterns of outgoing links determined by external slopes, etc. Our homophyly/kinship principle perfectly matches Darwin's observation that animals from ants to people form social groups in which most individuals work for the common good, and that kinship could encourage altruistic behavior. Our homophyly/kinship principle is the network version of Darwinian theory, and builds a bridge between Darwinian evolution and network science.

Similarity between community structures of different online social networks and its impact on underlying community detection

NASA Astrophysics Data System (ADS)

Fan, W.; Yeung, K. H.

2015-03-01

As social networking services are popular, many people may register in more than one online social network. In this paper we study a set of users who have accounts of three online social networks: namely Foursquare, Facebook and Twitter. Community structure of this set of users may be reflected in these three online social networks. Therefore, high correlation between these reflections and the underlying community structure may be observed. In this work, community structures are detected in all three online social networks. Also, we investigate the similarity level of community structures across different networks. It is found that they show strong correlation with each other. The similarity between different networks may be helpful to find a community structure close to the underlying one. To verify this, we propose a method to increase the weights of some connections in networks. With this method, new networks are generated to assist community detection. By doing this, value of modularity can be improved and the new community structure match network's natural structure better. In this paper we also show that the detected community structures of online social networks are correlated with users' locations which are identified on Foursquare. This information may also be useful for underlying community detection.

Overlapping community detection in weighted networks via a Bayesian approach

NASA Astrophysics Data System (ADS)

Chen, Yi; Wang, Xiaolong; Xiang, Xin; Tang, Buzhou; Chen, Qingcai; Fan, Shixi; Bu, Junzhao

2017-02-01

Complex networks as a powerful way to represent complex systems have been widely studied during the past several years. One of the most important tasks of complex network analysis is to detect communities embedded in networks. In the real world, weighted networks are very common and may contain overlapping communities where a node is allowed to belong to multiple communities. In this paper, we propose a novel Bayesian approach, called the Bayesian mixture network (BMN) model, to detect overlapping communities in weighted networks. The advantages of our method are (i) providing soft-partition solutions in weighted networks; (ii) providing soft memberships, which quantify 'how strongly' a node belongs to a community. Experiments on a large number of real and synthetic networks show that our model has the ability in detecting overlapping communities in weighted networks and is competitive with other state-of-the-art models at shedding light on community partition.

Social contagions on time-varying community networks

NASA Astrophysics Data System (ADS)

Liu, Mian-Xin; Wang, Wei; Liu, Ying; Tang, Ming; Cai, Shi-Min; Zhang, Hai-Feng

2017-05-01

Time-varying community structures exist widely in real-world networks. However, previous studies on the dynamics of spreading seldom took this characteristic into account, especially those on social contagions. To study the effects of time-varying community structures on social contagions, we propose a non-Markovian social contagion model on time-varying community networks based on the activity-driven network model. A mean-field theory is developed to analyze the proposed model. Through theoretical analyses and numerical simulations, two hierarchical features of the behavior adoption processes are found. That is, when community strength is relatively large, the behavior can easily spread in one of the communities, while in the other community the spreading only occurs at higher behavioral information transmission rates. Meanwhile, in spatial-temporal evolution processes, hierarchical orders are observed for the behavior adoption. Moreover, under different information transmission rates, three distinctive patterns are demonstrated in the change of the whole network's final adoption proportion along with the growing community strength. Within a suitable range of transmission rate, an optimal community strength can be found that can maximize the final adoption proportion. Finally, compared with the average activity potential, the promoting or inhibiting of social contagions is much more influenced by the number of edges generated by active nodes.

Online Community Detection for Large Complex Networks

PubMed Central

Pan, Gang; Zhang, Wangsheng; Wu, Zhaohui; Li, Shijian

2014-01-01

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance. PMID:25061683

Distributed network management in the flat structured mobile communities

NASA Astrophysics Data System (ADS)

Balandina, Elena

2005-10-01

Delivering proper management into the flat structured mobile communities is crucial for improving users experience and increase applications diversity in mobile networks. The available P2P applications do application-centric management, but it cannot replace network-wide management, especially when a number of different applications are used simultaneously in the network. The network-wide management is the key element required for a smooth transition from standalone P2P applications to the self-organizing mobile communities that maintain various services with quality and security guaranties. The classical centralized network management solutions are not applicable in the flat structured mobile communities due to the decentralized nature and high mobility of the underlying networks. Also the basic network management tasks have to be revised taking into account specialties of the flat structured mobile communities. The network performance management becomes more dependent on the current nodes' context, which also requires extension of the configuration management functionality. The fault management has to take into account high mobility of the network nodes. The performance and accounting managements are mainly targeted in maintain an efficient and fair access to the resources within the community, however they also allow unbalanced resource use of the nodes that explicitly permit it, e.g. as a voluntary donation to the community or due to the profession (commercial) reasons. The security management must implement the new trust models, which are based on the community feedback, professional authorization, and a mix of both. For fulfilling these and another specialties of the flat structured mobile communities, a new network management solution is demanded. The paper presents a distributed network management solution for flat structured mobile communities. Also the paper points out possible network management roles for the different parties (e.g. operators, service

Evolutionary method for finding communities in bipartite networks.

PubMed

Zhan, Weihua; Zhang, Zhongzhi; Guan, Jihong; Zhou, Shuigeng

2011-06-01

An important step in unveiling the relation between network structure and dynamics defined on networks is to detect communities, and numerous methods have been developed separately to identify community structure in different classes of networks, such as unipartite networks, bipartite networks, and directed networks. Here, we show that the finding of communities in such networks can be unified in a general framework-detection of community structure in bipartite networks. Moreover, we propose an evolutionary method for efficiently identifying communities in bipartite networks. To this end, we show that both unipartite and directed networks can be represented as bipartite networks, and their modularity is completely consistent with that for bipartite networks, the detection of modular structure on which can be reformulated as modularity maximization. To optimize the bipartite modularity, we develop a modified adaptive genetic algorithm (MAGA), which is shown to be especially efficient for community structure detection. The high efficiency of the MAGA is based on the following three improvements we make. First, we introduce a different measure for the informativeness of a locus instead of the standard deviation, which can exactly determine which loci mutate. This measure is the bias between the distribution of a locus over the current population and the uniform distribution of the locus, i.e., the Kullback-Leibler divergence between them. Second, we develop a reassignment technique for differentiating the informative state a locus has attained from the random state in the initial phase. Third, we present a modified mutation rule which by incorporating related operations can guarantee the convergence of the MAGA to the global optimum and can speed up the convergence process. Experimental results show that the MAGA outperforms existing methods in terms of modularity for both bipartite and unipartite networks.

A model for evolution of overlapping community networks

NASA Astrophysics Data System (ADS)

Karan, Rituraj; Biswal, Bibhu

2017-05-01

A model is proposed for the evolution of network topology in social networks with overlapping community structure. Starting from an initial community structure that is defined in terms of group affiliations, the model postulates that the subsequent growth and loss of connections is similar to the Hebbian learning and unlearning in the brain and is governed by two dominant factors: the strength and frequency of interaction between the members, and the degree of overlap between different communities. The temporal evolution from an initial community structure to the current network topology can be described based on these two parameters. It is possible to quantify the growth occurred so far and predict the final stationary state to which the network is likely to evolve. Applications in epidemiology or the spread of email virus in a computer network as well as finding specific target nodes to control it are envisaged. While facing the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities one faces the most basic questions: how do communities evolve in time? This work aims to address this issue by developing a mathematical model for the evolution of community networks and studying it through computer simulation.

A Community Information Network.

ERIC Educational Resources Information Center

Consumers' Association of Canada, Ottawa (Ontario).

The possibility of creating in Canada a non-profit community information network (a set of linked data banks containing information for use by the general public) should be explored. A network to link together a set of data banks containing information for general public use would have the following merits: (1) By its effect on household…

Taxonomies of networks from community structure

PubMed Central

Reid, Stephen; Porter, Mason A.; Mucha, Peter J.; Fricker, Mark D.; Jones, Nick S.

2014-01-01

The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: they can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi. PMID:23030977

Taxonomies of networks from community structure

NASA Astrophysics Data System (ADS)

Onnela, Jukka-Pekka; Fenn, Daniel J.; Reid, Stephen; Porter, Mason A.; Mucha, Peter J.; Fricker, Mark D.; Jones, Nick S.

2012-09-01

The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: They can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi.

21 CFR 800.12 - Contact lens solutions and tablets; tamper-resistant packaging.

Code of Federal Regulations, 2010 CFR

2010-04-01

...-resistant retail packages, there is the opportunity for the malicious adulteration of these products with... confidence in the security of the packages of over-the-counter (OTC) health care products. The Food and Drug... used to make such a solution for retail sale that is not packaged in a tamper-resistant package and...

21 CFR 800.12 - Contact lens solutions and tablets; tamper-resistant packaging.

Code of Federal Regulations, 2011 CFR

2011-04-01

...-resistant retail packages, there is the opportunity for the malicious adulteration of these products with... confidence in the security of the packages of over-the-counter (OTC) health care products. The Food and Drug... used to make such a solution for retail sale that is not packaged in a tamper-resistant package and...

21 CFR 800.12 - Contact lens solutions and tablets; tamper-resistant packaging.

Code of Federal Regulations, 2012 CFR

2012-04-01

...-resistant retail packages, there is the opportunity for the malicious adulteration of these products with... confidence in the security of the packages of over-the-counter (OTC) health care products. The Food and Drug... used to make such a solution for retail sale that is not packaged in a tamper-resistant package and...

21 CFR 800.12 - Contact lens solutions and tablets; tamper-resistant packaging.

Code of Federal Regulations, 2013 CFR

2013-04-01

...-resistant retail packages, there is the opportunity for the malicious adulteration of these products with... confidence in the security of the packages of over-the-counter (OTC) health care products. The Food and Drug... used to make such a solution for retail sale that is not packaged in a tamper-resistant package and...

21 CFR 800.12 - Contact lens solutions and tablets; tamper-resistant packaging.

Code of Federal Regulations, 2014 CFR

2014-04-01

...-resistant retail packages, there is the opportunity for the malicious adulteration of these products with... confidence in the security of the packages of over-the-counter (OTC) health care products. The Food and Drug... used to make such a solution for retail sale that is not packaged in a tamper-resistant package and...

Metabolic Network Modeling of Microbial Communities

PubMed Central

Biggs, Matthew B.; Medlock, Gregory L.; Kolling, Glynis L.

2015-01-01

Genome-scale metabolic network reconstructions and constraint-based analysis are powerful methods that have the potential to make functional predictions about microbial communities. Current use of genome-scale metabolic networks to characterize the metabolic functions of microbial communities includes species compartmentalization, separating species-level and community-level objectives, dynamic analysis, the “enzyme-soup” approach, multi-scale modeling, and others. There are many challenges inherent to the field, including a need for tools that accurately assign high-level omics signals to individual community members, new automated reconstruction methods that rival manual curation, and novel algorithms for integrating omics data and engineering communities. As technologies and modeling frameworks improve, we expect that there will be proportional advances in the fields of ecology, health science, and microbial community engineering. PMID:26109480

A new hierarchical method to find community structure in networks

NASA Astrophysics Data System (ADS)

Saoud, Bilal; Moussaoui, Abdelouahab

2018-04-01

Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.

Exploratory community sensing in social networks

NASA Astrophysics Data System (ADS)

Khrabrov, Alexy; Stocco, Gabriel; Cybenko, George

2010-04-01

Social networks generally provide an implementation of some kind of groups or communities which users can voluntarily join. Twitter does not have this functionality, and there is no notion of a formal group or community. We propose a method for identification of communities and assignment of semantic meaning to the discussion topics of the resulting communities. Using this analysis method and a sample of roughly a month's worth of Tweets from Twitter's "gardenhose" feed, we demonstrate the discovery of meaningful user communities on Twitter. We examine Twitter data streaming in real time and treat it as a sensor. Twitter is a social network which pioneered microblogging with the messages fitting an SMS, and a variety of clients, browsers, smart phones and PDAs are used for status updates by individuals, businesses, media outlets and even devices all over the world. Often an aggregate trend of such statuses may represent an important development in the world, which has been demonstrated with the Iran and Moldova elections and the anniversary of the Tiananmen in China. We propose using Twitter as a sensor, tracking individuals and communities of interest, and characterizing individual roles and dynamics of their communications. We developed a novel algorithm of community identification in social networks based on direct communication, as opposed to linking. We show ways to find communities of interest and then browse their neighborhoods by either similarity or diversity of individuals and groups adjacent to the one of interest. We use frequent collocations and statistically improbable phrases to summarize the focus of the community, giving a quick overview of its main topics. Our methods provide insight into the largest social sensor network in the world and constitute a platform for social sensing.

Identification of hybrid node and link communities in complex networks

PubMed Central

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-01-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately. PMID:25728010

Identification of hybrid node and link communities in complex networks.

PubMed

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-02

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

Identification of hybrid node and link communities in complex networks

NASA Astrophysics Data System (ADS)

He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

2015-03-01

Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

Information dynamics algorithm for detecting communities in networks

NASA Astrophysics Data System (ADS)

Massaro, Emanuele; Bagnoli, Franco; Guazzini, Andrea; Lió, Pietro

2012-11-01

The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method [4] by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.

Dynamics and control of diseases in networks with community structure.

PubMed

Salathé, Marcel; Jones, James H

2010-04-08

The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.

Interaction Networks: Generating High Level Hints Based on Network Community Clustering

ERIC Educational Resources Information Center

Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

2012-01-01

We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…

Uniquely identifiable tamper-evident device using coupling between subwavelength gratings

NASA Astrophysics Data System (ADS)

Fievre, Ange Marie Patricia

Reliability and sensitive information protection are critical aspects of integrated circuits. A novel technique using near-field evanescent wave coupling from two subwavelength gratings (SWGs), with the input laser source delivered through an optical fiber is presented for tamper evidence of electronic components. The first grating of the pair of coupled subwavelength gratings (CSWGs) was milled directly on the output facet of the silica fiber using focused ion beam (FIB) etching. The second grating was patterned using e-beam lithography and etched into a glass substrate using reactive ion etching (RIE). The slightest intrusion attempt would separate the CSWGs and eliminate near-field coupling between the gratings. Tampering, therefore, would become evident. Computer simulations guided the design for optimal operation of the security solution. The physical dimensions of the SWGs, i.e. period and thickness, were optimized, for a 650 nm illuminating wavelength. The optimal dimensions resulted in a 560 nm grating period for the first grating etched in the silica optical fiber and 420 nm for the second grating etched in borosilicate glass. The incident light beam had a half-width at half-maximum (HWHM) of at least 7 microm to allow discernible higher transmission orders, and a HWHM of 28 microm for minimum noise. The minimum number of individual grating lines present on the optical fiber facet was identified as 15 lines. Grating rotation due to the cylindrical geometry of the fiber resulted in a rotation of the far-field pattern, corresponding to the rotation angle of moire fringes. With the goal of later adding authentication to tamper evidence, the concept of CSWGs signature was also modeled by introducing random and planned variations in the glass grating. The fiber was placed on a stage supported by a nanomanipulator, which permitted three-dimensional displacement while maintaining the fiber tip normal to the surface of the glass substrate. A 650 nm diode laser was

Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks

PubMed Central

Taylor, Dane; Caceres, Rajmonda S.; Mucha, Peter J.

2017-01-01

Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős–Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection) and which occur for a given community provided its size surpasses a detectability limit K*. When layers are aggregated via a summation, we obtain K∗∝O(NL/T), where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L, then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than 𝒪(L−1/2). Moreover, we find that thresholding the summation can, in some cases, cause K* to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold. PMID:29445565

Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks.

PubMed

Taylor, Dane; Caceres, Rajmonda S; Mucha, Peter J

2017-01-01

Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős-Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection) and which occur for a given community provided its size surpasses a detectability limit K * . When layers are aggregated via a summation, we obtain [Formula: see text], where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L , then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than ( L -1/2 ). Moreover, we find that thresholding the summation can, in some cases, cause K * to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.

Information transfer in community structured multiplex networks

NASA Astrophysics Data System (ADS)

Solé Ribalta, Albert; Granell, Clara; Gómez, Sergio; Arenas, Alex

2015-08-01

The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

Community evolution mining and analysis in social network

NASA Astrophysics Data System (ADS)

Liu, Hongtao; Tian, Yuan; Liu, Xueyan; Jian, Jie

2017-03-01

With the development of digital and network technology, various social platforms emerge. These social platforms have greatly facilitated access to information, attracting more and more users. They use these social platforms every day to work, study and communicate, so every moment social platforms are generating massive amounts of data. These data can often be modeled as complex networks, making large-scale social network analysis possible. In this paper, the existing evolution classification model of community has been improved based on community evolution relationship over time in dynamic social network, and the Evolution-Tree structure is proposed which can show the whole life cycle of the community more clearly. The comparative test result shows that the improved model can excavate the evolution relationship of the community well.

Program Spotlight: National Outreach Network's Community Health Educators

Cancer.gov

National Outreach Network of Community Health Educators located at Community Network Program Centers, Partnerships to Advance Cancer Health Equity, and NCI-designated cancer centers help patients and their families receive survivorship support.

Building research infrastructure in community health centers: a Community Health Applied Research Network (CHARN) report.

PubMed

Likumahuwa, Sonja; Song, Hui; Singal, Robbie; Weir, Rosy Chang; Crane, Heidi; Muench, John; Sim, Shao-Chee; DeVoe, Jennifer E

2013-01-01

This article introduces the Community Health Applied Research Network (CHARN), a practice-based research network of community health centers (CHCs). Established by the Health Resources and Services Administration in 2010, CHARN is a network of 4 community research nodes, each with multiple affiliated CHCs and an academic center. The four nodes (18 individual CHCs and 4 academic partners in 9 states) are supported by a data coordinating center. Here we provide case studies detailing how CHARN is building research infrastructure and capacity in CHCs, with a particular focus on how community practice-academic partnerships were facilitated by the CHARN structure. The examples provided by the CHARN nodes include many of the building blocks of research capacity: communication capacity and "matchmaking" between providers and researchers; technology transfer; research methods tailored to community practice settings; and community institutional review board infrastructure to enable community oversight. We draw lessons learned from these case studies that we hope will serve as examples for other networks, with special relevance for community-based networks seeking to build research infrastructure in primary care settings.

Building Research Infrastructure in Community Health Centers: A Community Health Applied Research Network (CHARN) Report

PubMed Central

Likumahuwa, Sonja; Song, Hui; Singal, Robbie; Weir, Rosy Chang; Crane, Heidi; Muench, John; Sim, Shao-Chee; DeVoe, Jennifer E.

2015-01-01

This article introduces the Community Health Applied Research Network (CHARN), a practice-based research network of community health centers (CHCs). Established by the Health Resources and Services Administration in 2010, CHARN is a network of 4 community research nodes, each with multiple affiliated CHCs and an academic center. The four nodes (18 individual CHCs and 4 academic partners in 9 states) are supported by a data coordinating center. Here we provide case studies detailing how CHARN is building research infrastructure and capacity in CHCs, with a particular focus on how community practice-academic partnerships were facilitated by the CHARN structure. The examples provided by the CHARN nodes include many of the building blocks of research capacity: communication capacity and “matchmaking” between providers and researchers; technology transfer; research methods tailored to community practice settings; and community institutional review board infrastructure to enable community oversight. We draw lessons learned from these case studies that we hope will serve as examples for other networks, with special relevance for community-based networks seeking to build research infrastructure in primary care settings. PMID:24004710

Detecting network communities beyond assortativity-related attributes

NASA Astrophysics Data System (ADS)

Liu, Xin; Murata, Tsuyoshi; Wakita, Ken

2014-07-01

In network science, assortativity refers to the tendency of links to exist between nodes with similar attributes. In social networks, for example, links tend to exist between individuals of similar age, nationality, location, race, income, educational level, religious belief, and language. Thus, various attributes jointly affect the network topology. An interesting problem is to detect community structure beyond some specific assortativity-related attributes ρ, i.e., to take out the effect of ρ on network topology and reveal the hidden community structures which are due to other attributes. An approach to this problem is to redefine the null model of the modularity measure, so as to simulate the effect of ρ on network topology. However, a challenge is that we do not know to what extent the network topology is affected by ρ and by other attributes. In this paper, we propose a distance modularity, which allows us to freely choose any suitable function to simulate the effect of ρ. Such freedom can help us probe the effect of ρ and detect the hidden communities which are due to other attributes. We test the effectiveness of distance modularity on synthetic benchmarks and two real-world networks.

Sociospatial Knowledge Networks: Appraising Community as Place.

ERIC Educational Resources Information Center

Skelly, Anne H.; Arcury, Thomas A.; Gesler, Wilbert M.; Cravey, Altha J.; Dougherty, Molly C.; Washburn, Sarah A.; Nash, Sally

2002-01-01

A new theory of geographical analysis--sociospatial knowledge networks--provides a framework for understanding the social and spatial locations of a community's health knowledge and beliefs. This theory is guiding an ethnographic study of health beliefs, knowledge, and knowledge networks in a diverse rural community at high risk for type-2…

Incorporating profile information in community detection for online social networks

NASA Astrophysics Data System (ADS)

Fan, W.; Yeung, K. H.

2014-07-01

Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users' profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.

Sociospatial knowledge networks: appraising community as place.

PubMed

Skelly, Anne H; Arcury, Thomas A; Gesler, Wilbert M; Cravey, Altha J; Dougherty, Molly C; Washburn, Sarah A; Nash, Sally

2002-04-01

This article introduces a new theory of geographical analysis, sociospatial knowledge networks, for examining and understanding the spatial aspects of health knowledge (i.e., exactly where health beliefs and knowledge coincide with other support in the community). We present an overview of the theory of sociospatial knowledge networks and an example of how it is being used to guide an ongoing ethnographic study of health beliefs, knowledge, and knowledge networks in a rural community of African Americans, Latinos, and European Americans at high risk for, but not diagnosed with, type 2 diabetes mellitus. We believe that the geographical approach to understanding health beliefs and knowledge and how people acquire health information presented here is one that could serve other communities and community health practitioners working to improve chronic disease outcomes in diverse local environments. Copyright 2002 Wiley Periodicals, Inc.

Epidemic spreading in time-varying community networks.

PubMed

Ren, Guangming; Wang, Xingyuan

2014-06-01

The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold qc. The epidemic will survive when q > qc and die when q < qc. These results can help understanding the impacts of human travel on the epidemic spreading in complex networks with community structure.

Detecting and analyzing research communities in longitudinal scientific networks.

PubMed

Leone Sciabolazza, Valerio; Vacca, Raffaele; Kennelly Okraku, Therese; McCarty, Christopher

2017-01-01

A growing body of evidence shows that collaborative teams and communities tend to produce the highest-impact scientific work. This paper proposes a new method to (1) Identify collaborative communities in longitudinal scientific networks, and (2) Evaluate the impact of specific research institutes, services or policies on the interdisciplinary collaboration between these communities. First, we apply community-detection algorithms to cross-sectional scientific collaboration networks and analyze different types of co-membership in the resulting subgroups over time. This analysis summarizes large amounts of longitudinal network data to extract sets of research communities whose members have consistently collaborated or shared collaborators over time. Second, we construct networks of cross-community interactions and estimate Exponential Random Graph Models to predict the formation of interdisciplinary collaborations between different communities. The method is applied to longitudinal data on publication and grant collaborations at the University of Florida. Results show that similar institutional affiliation, spatial proximity, transitivity effects, and use of the same research services predict higher degree of interdisciplinary collaboration between research communities. Our application also illustrates how the identification of research communities in longitudinal data and the analysis of cross-community network formation can be used to measure the growth of interdisciplinary team science at a research university, and to evaluate its association with research policies, services or institutes.

Detecting and analyzing research communities in longitudinal scientific networks

PubMed Central

Vacca, Raffaele; Kennelly Okraku, Therese; McCarty, Christopher

2017-01-01

A growing body of evidence shows that collaborative teams and communities tend to produce the highest-impact scientific work. This paper proposes a new method to (1) Identify collaborative communities in longitudinal scientific networks, and (2) Evaluate the impact of specific research institutes, services or policies on the interdisciplinary collaboration between these communities. First, we apply community-detection algorithms to cross-sectional scientific collaboration networks and analyze different types of co-membership in the resulting subgroups over time. This analysis summarizes large amounts of longitudinal network data to extract sets of research communities whose members have consistently collaborated or shared collaborators over time. Second, we construct networks of cross-community interactions and estimate Exponential Random Graph Models to predict the formation of interdisciplinary collaborations between different communities. The method is applied to longitudinal data on publication and grant collaborations at the University of Florida. Results show that similar institutional affiliation, spatial proximity, transitivity effects, and use of the same research services predict higher degree of interdisciplinary collaboration between research communities. Our application also illustrates how the identification of research communities in longitudinal data and the analysis of cross-community network formation can be used to measure the growth of interdisciplinary team science at a research university, and to evaluate its association with research policies, services or institutes. PMID:28797047

The Community Science Workshop Network Story: Becoming a Networked Organization

ERIC Educational Resources Information Center

St. John, Mark

2014-01-01

The Community Science Workshops (CSWs)--with funding from the S.D. Bechtel, Jr. Foundation, and the Gordon and Betty Moore Foundation--created a network among the CSW sites in California. The goals of the CSW Network project have been to improve programs, build capacity throughout the Network, and establish new sites. Inverness Research has been…

Distributed learning automata-based algorithm for community detection in complex networks

NASA Astrophysics Data System (ADS)

Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

2016-03-01

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

A cooperative game framework for detecting overlapping communities in social networks

NASA Astrophysics Data System (ADS)

Jonnalagadda, Annapurna; Kuppusamy, Lakshmanan

2018-02-01

Community detection in social networks is a challenging and complex task, which received much attention from researchers of multiple domains in recent years. The evolution of communities in social networks happens merely due to the self-interest of the nodes. The interesting feature of community structure in social networks is the multi membership of the nodes resulting in overlapping communities. Assuming the nodes of the social network as self-interested players, the dynamics of community formation can be captured in the form of a game. In this paper, we propose a greedy algorithm, namely, Weighted Graph Community Game (WGCG), in order to model the interactions among the self-interested nodes of the social network. The proposed algorithm employs the Shapley value mechanism to discover the inherent communities of the underlying social network. The experimental evaluation on the real-world and synthetic benchmark networks demonstrates that the performance of the proposed algorithm is superior to the state-of-the-art overlapping community detection algorithms.

Community detection for networks with unipartite and bipartite structure

NASA Astrophysics Data System (ADS)

Chang, Chang; Tang, Chao

2014-09-01

Finding community structures in networks is important in network science, technology, and applications. To date, most algorithms that aim to find community structures only focus either on unipartite or bipartite networks. A unipartite network consists of one set of nodes and a bipartite network consists of two nonoverlapping sets of nodes with only links joining the nodes in different sets. However, a third type of network exists, defined here as the mixture network. Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the nonoverlapping restriction of a bipartite network. The mixture network can be considered as a general case, with unipartite and bipartite networks viewed as its limiting cases. A mixture network can represent not only all the unipartite and bipartite networks, but also a wide range of real-world networks that cannot be properly represented as either unipartite or bipartite networks in fields such as biology and social science. Based on this observation, we first propose a probabilistic model that can find modules in unipartite, bipartite, and mixture networks in a unified framework based on the link community model for a unipartite undirected network [B Ball et al (2011 Phys. Rev. E 84 036103)]. We test our algorithm on synthetic networks (both overlapping and nonoverlapping communities) and apply it to two real-world networks: a southern women bipartite network and a human transcriptional regulatory mixture network. The results suggest that our model performs well for all three types of networks, is competitive with other algorithms for unipartite or bipartite networks, and is applicable to real-world networks.

Z-Score-Based Modularity for Community Detection in Networks

PubMed Central

Miyauchi, Atsushi; Kawase, Yasushi

2016-01-01

Identifying community structure in networks is an issue of particular interest in network science. The modularity introduced by Newman and Girvan is the most popular quality function for community detection in networks. In this study, we identify a problem in the concept of modularity and suggest a solution to overcome this problem. Specifically, we obtain a new quality function for community detection. We refer to the function as Z-modularity because it measures the Z-score of a given partition with respect to the fraction of the number of edges within communities. Our theoretical analysis shows that Z-modularity mitigates the resolution limit of the original modularity in certain cases. Computational experiments using both artificial networks and well-known real-world networks demonstrate the validity and reliability of the proposed quality function. PMID:26808270

Adaptive multi-resolution Modularity for detecting communities in networks

NASA Astrophysics Data System (ADS)

Chen, Shi; Wang, Zhi-Zhong; Bao, Mei-Hua; Tang, Liang; Zhou, Ji; Xiang, Ju; Li, Jian-Ming; Yi, Chen-He

2018-02-01

Community structure is a common topological property of complex networks, which attracted much attention from various fields. Optimizing quality functions for community structures is a kind of popular strategy for community detection, such as Modularity optimization. Here, we introduce a general definition of Modularity, by which several classical (multi-resolution) Modularity can be derived, and then propose a kind of adaptive (multi-resolution) Modularity that can combine the advantages of different Modularity. By applying the Modularity to various synthetic and real-world networks, we study the behaviors of the methods, showing the validity and advantages of the multi-resolution Modularity in community detection. The adaptive Modularity, as a kind of multi-resolution method, can naturally solve the first-type limit of Modularity and detect communities at different scales; it can quicken the disconnecting of communities and delay the breakup of communities in heterogeneous networks; and thus it is expected to generate the stable community structures in networks more effectively and have stronger tolerance against the second-type limit of Modularity.

Social network fragmentation and community health.

PubMed

Chami, Goylette F; Ahnert, Sebastian E; Kabatereine, Narcis B; Tukahebwa, Edridah M

2017-09-05

Community health interventions often seek to intentionally destroy paths between individuals to prevent the spread of infectious diseases. Immunizing individuals through direct vaccination or the provision of health education prevents pathogen transmission and the propagation of misinformation concerning medical treatments. However, it remains an open question whether network-based strategies should be used in place of conventional field approaches to target individuals for medical treatment in low-income countries. We collected complete friendship and health advice networks in 17 rural villages of Mayuge District, Uganda. Here we show that acquaintance algorithms, i.e., selecting neighbors of randomly selected nodes, were systematically more efficient in fragmenting all networks than targeting well-established community roles, i.e., health workers, village government members, and schoolteachers. Additionally, community roles were not good proxy indicators of physical proximity to other households or connections to many sick people. We also show that acquaintance algorithms were effective in offsetting potential noncompliance with deworming treatments for 16,357 individuals during mass drug administration (MDA). Health advice networks were destroyed more easily than friendship networks. Only an average of 32% of nodes were removed from health advice networks to reduce the percentage of nodes at risk for refusing treatment in MDA to below 25%. Treatment compliance of at least 75% is needed in MDA to control human morbidity attributable to parasitic worms and progress toward elimination. Our findings point toward the potential use of network-based approaches as an alternative to role-based strategies for targeting individuals in rural health interventions.

The evolution of communities in the international oil trade network

NASA Astrophysics Data System (ADS)

Zhong, Weiqiong; An, Haizhong; Gao, Xiangyun; Sun, Xiaoqi

2014-11-01

International oil trade is a subset of global trade and there exist oil trade communities. These communities evolve over time and provide clues of international oil trade patterns. A better understanding of the international oil trade patterns is necessary for governments in policy making. To study the evolution of trade communities in the international oil trade network, we set up unweighted and weighted oil trade network models based on complex network theory using data from 2002 to 2011. We detected the communities in the oil trade networks and analyzed their evolutionary properties and stabilities over time. We found that the unweighted and weighted international oil trade networks show many different features in terms of community number, community scale, distribution of countries, quality of partitions, and stability of communities. Two turning points occurred in the evolution of community stability in the international oil trade network. One is the year 2004-2005 which correlates with changes in demand and supply in the world oil market after the Iraq War, and the other is the year 2008-2009 which is connected to the 2008 financial crisis. Different causations of instability show different features and this should be considered by policy makers.

Epidemic spreading in time-varying community networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ren, Guangming, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com; Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024; Wang, Xingyuan, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com

2014-06-15

The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold q{sub c}. The epidemic will survive when q > q{sub c} and die when q < q{sub c}. These results can help understanding the impacts of human travel onmore » the epidemic spreading in complex networks with community structure.« less

Identifying influential user communities on the social network

NASA Astrophysics Data System (ADS)

Hu, Weishu; Gong, Zhiguo; Hou U, Leong; Guo, Jingzhi

2015-10-01

Nowadays social network services have been popularly used in electronic commerce systems. Users on the social network can develop different relationships based on their common interests and activities. In order to promote the business, it is interesting to explore hidden relationships among users developed on the social network. Such knowledge can be used to locate target users for different advertisements and to provide effective product recommendations. In this paper, we define and study a novel community detection problem that is to discover the hidden community structure in large social networks based on their common interests. We observe that the users typically pay more attention to those users who share similar interests, which enable a way to partition the users into different communities according to their common interests. We propose two algorithms to detect influential communities using common interests in large social networks efficiently and effectively. We conduct our experimental evaluation using a data set from Epinions, which demonstrates that our method achieves 4-11.8% accuracy improvement over the state-of-the-art method.

The Virginia Community Cadre Network: Community Reintegration of Persons with Spinal Cord Injury.

ERIC Educational Resources Information Center

Wilson, Walter C.; Thompson, Donald D.

1983-01-01

The Community Cadre Network in Virginia is a local support network for helping individuals with spinal cord injuries make the transition from an institutional setting to home and community life. Cadre members are people who have been through the experience of traumatic injury, rehabilitation, and return home. (SEW)

Correlations between Community Structure and Link Formation in Complex Networks

PubMed Central

Liu, Zhen; He, Jia-Lin; Kapoor, Komal; Srivastava, Jaideep

2013-01-01

Background Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. Methodology/Principal Findings Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. Conclusions/Significance Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction. PMID:24039818

The ground truth about metadata and community detection in networks.

PubMed

Peel, Leto; Larremore, Daniel B; Clauset, Aaron

2017-05-01

Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks' links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures.

A framework for detecting communities of unbalanced sizes in networks

NASA Astrophysics Data System (ADS)

Žalik, Krista Rizman; Žalik, Borut

2018-01-01

Community detection in large networks has been a focus of recent research in many of fields, including biology, physics, social sciences, and computer science. Most community detection methods partition the entire network into communities, groups of nodes that have many connections within communities and few connections between them and do not identify different roles that nodes can have in communities. We propose a community detection model that integrates more different measures that can fast identify communities of different sizes and densities. We use node degree centrality, strong similarity with one node from community, maximal similarity of node to community, compactness of communities and separation between communities. Each measure has its own strength and weakness. Thus, combining different measures can benefit from the strengths of each one and eliminate encountered problems of using an individual measure. We present a fast local expansion algorithm for uncovering communities of different sizes and densities and reveals rich information on input networks. Experimental results show that the proposed algorithm is better or as effective as the other community detection algorithms for both real-world and synthetic networks while it requires less time.

Stylized facts in social networks: Community-based static modeling

NASA Astrophysics Data System (ADS)

Jo, Hang-Hyun; Murase, Yohsuke; Török, János; Kertész, János; Kaski, Kimmo

2018-06-01

The past analyses of datasets of social networks have enabled us to make empirical findings of a number of aspects of human society, which are commonly featured as stylized facts of social networks, such as broad distributions of network quantities, existence of communities, assortative mixing, and intensity-topology correlations. Since the understanding of the structure of these complex social networks is far from complete, for deeper insight into human society more comprehensive datasets and modeling of the stylized facts are needed. Although the existing dynamical and static models can generate some stylized facts, here we take an alternative approach by devising a community-based static model with heterogeneous community sizes and larger communities having smaller link density and weight. With these few assumptions we are able to generate realistic social networks that show most stylized facts for a wide range of parameters, as demonstrated numerically and analytically. Since our community-based static model is simple to implement and easily scalable, it can be used as a reference system, benchmark, or testbed for further applications.

Evolution properties of the community members for dynamic networks

NASA Astrophysics Data System (ADS)

Yang, Kai; Guo, Qiang; Li, Sheng-Nan; Han, Jing-Ti; Liu, Jian-Guo

2017-03-01

The collective behaviors of community members for dynamic social networks are significant for understanding evolution features of communities. In this Letter, we empirically investigate the evolution properties of the new community members for dynamic networks. Firstly, we separate data sets into different slices, and analyze the statistical properties of new members as well as communities they joined in for these data sets. Then we introduce a parameter φ to describe community evolution between different slices and investigate the dynamic community properties of the new community members. The empirical analyses for the Facebook, APS, Enron and Wiki data sets indicate that both the number of new members and joint communities increase, the ratio declines rapidly and then becomes stable over time, and most of the new members will join in the small size communities that is s ≤ 10. Furthermore, the proportion of new members in existed communities decreases firstly and then becomes stable and relatively small for these data sets. Our work may be helpful for deeply understanding the evolution properties of community members for social networks.

The Imperial County Community Air Monitoring Network: A Model for Community-based Environmental Monitoring for Public Health Action

PubMed Central

Olmedo, Luis; Bejarano, Ester; Lugo, Humberto; Murillo, Eduardo; Seto, Edmund; Wong, Michelle; King, Galatea; Wilkie, Alexa; Meltzer, Dan; Carvlin, Graeme; Jerrett, Michael; Northcross, Amanda

2017-01-01

Summary: The Imperial County Community Air Monitoring Network (the Network) is a collaborative group of community, academic, nongovernmental, and government partners designed to fill the need for more detailed data on particulate matter in an area that often exceeds air quality standards. The Network employs a community-based environmental monitoring process in which the community and researchers have specific, well-defined roles as part of an equitable partnership that also includes shared decision-making to determine study direction, plan research protocols, and conduct project activities. The Network is currently producing real-time particulate matter data from 40 low-cost sensors throughout Imperial County, one of the largest community-based air networks in the United States. Establishment of a community-led air network involves engaging community members to be citizen-scientists in the monitoring, siting, and data collection process. Attention to technical issues regarding instrument calibration and validation and electronic transfer and storage of data is also essential. Finally, continued community health improvements will be predicated on facilitating community ownership and sustainability of the network after research funds have been expended. https://doi.org/10.1289/EHP1772 PMID:28886604

Community Structure in Online Collegiate Social Networks

NASA Astrophysics Data System (ADS)

Traud, Amanda; Kelsic, Eric; Mucha, Peter; Porter, Mason

2009-03-01

Online social networking sites have become increasingly popular with college students. The networks we studied are defined through ``friendships'' indicated by Facebook users from UNC, Oklahoma, Caltech, Georgetown, and Princeton. We apply the tools of network science to study the Facebook networks from these five different universities at a single point in time. We investigate each single-institution network's community structure, which we obtain through partitioning the graph using an eigenvector method. We use both graphical and quantitative tools, including pair-counting methods, which we interpret through statistical analysis and permutation tests to measure the correlations between the network communities and a set of characteristics given by each user (residence, class year, major, and high school). We also analyze the single gender subsets of these networks, and the impact of missing demographical data. Our study allows us to compare the online social networks for the five schools as well as infer differences in offline social interactions. At the schools studied, we were able to define which characteristics of the Facebook users correlate best with friendships.

Message Integrity Model for Wireless Sensor Networks

ERIC Educational Resources Information Center

Qleibo, Haider W.

2009-01-01

WSNs are susceptible to a variety of attacks. These attacks vary in the way they are performed and executed; they include but not limited to node capture, physical tampering, denial of service, and message alteration. It is of paramount importance to protect gathered data by WSNs and defend the network against illegal access and malicious…

Learning Networks--Enabling Change through Community Action Research

ERIC Educational Resources Information Center

Bleach, Josephine

2016-01-01

Learning networks are a critical element of ethos of the community action research approach taken by the Early Learning Initiative at the National College of Ireland, a community-based educational initiative in the Dublin Docklands. Key criteria for networking, whether at local, national or international level, are the individual's and…

KDU E-Community Network.

ERIC Educational Resources Information Center

Jonhendro; Ching, Goh Bee; Wahab, Rohazna; Leng, Wang Meei; Aun, Jimmy Tan Lip; Yeoh, Eugene; Hock, Oon; Koo, W. K.

2001-01-01

Describes an education initiative developed by a company in Malaysia, the KDU, to implement a student-centered, teacher-facilitated, educational technology-enabled and knowledge-based learning environment. Explains the KDU e-Community Network that enables passive, interactive, collaborative, and constructivist learning for a variety of…

Covariance, correlation matrix, and the multiscale community structure of networks.

PubMed

Shen, Hua-Wei; Cheng, Xue-Qi; Fang, Bin-Xing

2010-07-01

Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this paper, we consider detecting the multiscale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multiscale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multiscale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multiscale community structure of network, as well as the translation and rotation transformations.

Mixture models with entropy regularization for community detection in networks

NASA Astrophysics Data System (ADS)

Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang

2018-04-01

Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

SA-SOM algorithm for detecting communities in complex networks

NASA Astrophysics Data System (ADS)

Chen, Luogeng; Wang, Yanran; Huang, Xiaoming; Hu, Mengyu; Hu, Fang

2017-10-01

Currently, community detection is a hot topic. This paper, based on the self-organizing map (SOM) algorithm, introduced the idea of self-adaptation (SA) that the number of communities can be identified automatically, a novel algorithm SA-SOM of detecting communities in complex networks is proposed. Several representative real-world networks and a set of computer-generated networks by LFR-benchmark are utilized to verify the accuracy and the efficiency of this algorithm. The experimental findings demonstrate that this algorithm can identify the communities automatically, accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity, NMI and density than the SOM algorithm does.

Detecting communities in large networks

NASA Astrophysics Data System (ADS)

Capocci, A.; Servedio, V. D. P.; Caldarelli, G.; Colaiori, F.

2005-07-01

We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns.

Emergence of Multiplex Communities in Collaboration Networks.

PubMed

Battiston, Federico; Iacovacci, Jacopo; Nicosia, Vincenzo; Bianconi, Ginestra; Latora, Vito

2016-01-01

Community structures in collaboration networks reflect the natural tendency of individuals to organize their work in groups in order to better achieve common goals. In most of the cases, individuals exploit their connections to introduce themselves to new areas of interests, giving rise to multifaceted collaborations which span different fields. In this paper, we analyse collaborations in science and among movie actors as multiplex networks, where the layers represent respectively research topics and movie genres, and we show that communities indeed coexist and overlap at the different layers of such systems. We then propose a model to grow multiplex networks based on two mechanisms of intra and inter-layer triadic closure which mimic the real processes by which collaborations evolve. We show that our model is able to explain the multiplex community structure observed empirically, and we infer the strength of the two underlying social mechanisms from real-world systems. Being also able to correctly reproduce the values of intra-layer and inter-layer assortativity correlations, the model contributes to a better understanding of the principles driving the evolution of social networks.

Community detection in networks: A user guide

NASA Astrophysics Data System (ADS)

Fortunato, Santo; Hric, Darko

2016-11-01

Community detection in networks is one of the most popular topics of modern network science. Communities, or clusters, are usually groups of vertices having higher probability of being connected to each other than to members of other groups, though other patterns are possible. Identifying communities is an ill-defined problem. There are no universal protocols on the fundamental ingredients, like the definition of community itself, nor on other crucial issues, like the validation of algorithms and the comparison of their performances. This has generated a number of confusions and misconceptions, which undermine the progress in the field. We offer a guided tour through the main aspects of the problem. We also point out strengths and weaknesses of popular methods, and give directions to their use.

Game Theoretical Analysis on Cooperation Stability and Incentive Effectiveness in Community Networks.

PubMed

Song, Kaida; Wang, Rui; Liu, Yi; Qian, Depei; Zhang, Han; Cai, Jihong

2015-01-01

Community networks, the distinguishing feature of which is membership admittance, appear on P2P networks, social networks, and conventional Web networks. Joining the network costs money, time or network bandwidth, but the individuals get access to special resources owned by the community in return. The prosperity and stability of the community are determined by both the policy of admittance and the attraction of the privileges gained by joining. However, some misbehaving users can get the dedicated resources with some illicit and low-cost approaches, which introduce instability into the community, a phenomenon that will destroy the membership policy. In this paper, we analyze on the stability using game theory on such a phenomenon. We propose a game-theoretical model of stability analysis in community networks and provide conditions for a stable community. We then extend the model to analyze the effectiveness of different incentive policies, which could be used when the community cannot maintain its members in certain situations. Then we verify those models through a simulation. Finally, we discuss several ways to promote community network's stability by adjusting the network's properties and give some proposal on the designs of these types of networks from the points of game theory and stability.

Local community detection as pattern restoration by attractor dynamics of recurrent neural networks.

PubMed

Okamoto, Hiroshi

2016-08-01

Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of "true" members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., "false" members of the community). We propose to detect the community from this "imperfect" and "inaccurate" set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Community structure from spectral properties in complex networks

NASA Astrophysics Data System (ADS)

Servedio, V. D. P.; Colaiori, F.; Capocci, A.; Caldarelli, G.

2005-06-01

We analyze the spectral properties of complex networks focusing on their relation to the community structure, and develop an algorithm based on correlations among components of different eigenvectors. The algorithm applies to general weighted networks, and, in a suitably modified version, to the case of directed networks. Our method allows to correctly detect communities in sharply partitioned graphs, however it is useful to the analysis of more complex networks, without a well defined cluster structure, as social and information networks. As an example, we test the algorithm on a large scale data-set from a psychological experiment of free word association, where it proves to be successful both in clustering words, and in uncovering mental association patterns.

SCOUT: simultaneous time segmentation and community detection in dynamic networks

PubMed Central

Hulovatyy, Yuriy; Milenković, Tijana

2016-01-01

Many evolving complex real-world systems can be modeled via dynamic networks. An important problem in dynamic network research is community detection, which finds groups of topologically related nodes. Typically, this problem is approached by assuming either that each time point has a distinct community organization or that all time points share a single community organization. The reality likely lies between these two extremes. To find the compromise, we consider community detection in the context of the problem of segment detection, which identifies contiguous time periods with consistent network structure. Consequently, we formulate a combined problem of segment community detection (SCD), which simultaneously partitions the network into contiguous time segments with consistent community organization and finds this community organization for each segment. To solve SCD, we introduce SCOUT, an optimization framework that explicitly considers both segmentation quality and partition quality. SCOUT addresses limitations of existing methods that can be adapted to solve SCD, which consider only one of segmentation quality or partition quality. In a thorough evaluation, SCOUT outperforms the existing methods in terms of both accuracy and computational complexity. We apply SCOUT to biological network data to study human aging. PMID:27881879

Epidemic spreading on complex networks with overlapping and non-overlapping community structure

NASA Astrophysics Data System (ADS)

Shang, Jiaxing; Liu, Lianchen; Li, Xin; Xie, Feng; Wu, Cheng

2015-02-01

Many real-world networks exhibit community structure where vertices belong to one or more communities. Recent studies show that community structure plays an import role in epidemic spreading. In this paper, we investigate how the extent of overlap among communities affects epidemics. In order to experiment on the characteristic of overlapping communities, we propose a rewiring algorithm that can change the community structure from overlapping to non-overlapping while maintaining the degree distribution of the network. We simulate the Susceptible-Infected-Susceptible (SIS) epidemic process on synthetic scale-free networks and real-world networks by applying our rewiring algorithm. Experiments show that epidemics spread faster on networks with higher level of overlapping communities. Furthermore, overlapping communities' effect interacts with the average degree's effect. Our work further illustrates the important role of overlapping communities in the process of epidemic spreading.

The ground truth about metadata and community detection in networks

PubMed Central

Peel, Leto; Larremore, Daniel B.; Clauset, Aaron

2017-01-01

Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system’s components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks’ links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures. PMID:28508065

Community detection in sequence similarity networks based on attribute clustering

DOE PAGES

Chowdhary, Janamejaya; Loeffler, Frank E.; Smith, Jeremy C.

2017-07-24

Networks are powerful tools for the presentation and analysis of interactions in multi-component systems. A commonly studied mesoscopic feature of networks is their community structure, which arises from grouping together similar nodes into one community and dissimilar nodes into separate communities. Here in this paper, the community structure of protein sequence similarity networks is determined with a new method: Attribute Clustering Dependent Communities (ACDC). Sequence similarity has hitherto typically been quantified by the alignment score or its expectation value. However, pair alignments with the same score or expectation value cannot thus be differentiated. To overcome this deficiency, the method constructs,more » for pair alignments, an extended alignment metric, the link attribute vector, which includes the score and other alignment characteristics. Rescaling components of the attribute vectors qualitatively identifies a systematic variation of sequence similarity within protein superfamilies. The problem of community detection is then mapped to clustering the link attribute vectors, selection of an optimal subset of links and community structure refinement based on the partition density of the network. ACDC-predicted communities are found to be in good agreement with gold standard sequence databases for which the "ground truth" community structures (or families) are known. ACDC is therefore a community detection method for sequence similarity networks based entirely on pair similarity information. A serial implementation of ACDC is available from https://cmb.ornl.gov/resources/developments« less

Community detection in sequence similarity networks based on attribute clustering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chowdhary, Janamejaya; Loeffler, Frank E.; Smith, Jeremy C.

Networks are powerful tools for the presentation and analysis of interactions in multi-component systems. A commonly studied mesoscopic feature of networks is their community structure, which arises from grouping together similar nodes into one community and dissimilar nodes into separate communities. Here in this paper, the community structure of protein sequence similarity networks is determined with a new method: Attribute Clustering Dependent Communities (ACDC). Sequence similarity has hitherto typically been quantified by the alignment score or its expectation value. However, pair alignments with the same score or expectation value cannot thus be differentiated. To overcome this deficiency, the method constructs,more » for pair alignments, an extended alignment metric, the link attribute vector, which includes the score and other alignment characteristics. Rescaling components of the attribute vectors qualitatively identifies a systematic variation of sequence similarity within protein superfamilies. The problem of community detection is then mapped to clustering the link attribute vectors, selection of an optimal subset of links and community structure refinement based on the partition density of the network. ACDC-predicted communities are found to be in good agreement with gold standard sequence databases for which the "ground truth" community structures (or families) are known. ACDC is therefore a community detection method for sequence similarity networks based entirely on pair similarity information. A serial implementation of ACDC is available from https://cmb.ornl.gov/resources/developments« less

Ubiquitousness of link-density and link-pattern communities in real-world networks

NASA Astrophysics Data System (ADS)

Šubelj, L.; Bajec, M.

2012-01-01

Community structure appears to be an intrinsic property of many complex real-world networks. However, recent work shows that real-world networks reveal even more sophisticated modules than classical cohesive (link-density) communities. In particular, networks can also be naturally partitioned according to similar patterns of connectedness among the nodes, revealing link-pattern communities. We here propose a propagation based algorithm that can extract both link-density and link-pattern communities, without any prior knowledge of the true structure. The algorithm was first validated on different classes of synthetic benchmark networks with community structure, and also on random networks. We have further applied the algorithm to different social, information, technological and biological networks, where it indeed reveals meaningful (composites of) link-density and link-pattern communities. The results thus seem to imply that, similarly as link-density counterparts, link-pattern communities appear ubiquitous in nature and design.

Apparatus and method for detecting tampering in flexible structures

DOEpatents

Maxey, Lonnie C [Knoxville, TN; Haynes, Howard D [Knoxville, TN

2011-02-01

A system for monitoring or detecting tampering in a flexible structure includes taking electrical measurements on a sensing cable coupled to the structure, performing spectral analysis on the measured data, and comparing the spectral characteristics of the event to those of known benign and/or known suspicious events. A threshold or trigger value may used to identify an event of interest and initiate data collection. Alternatively, the system may be triggered at preset intervals, triggered manually, or triggered by a signal from another sensing device such as a motion detector. The system may be used to monitor electrical cables and conduits, hoses and flexible ducts, fences and other perimeter control devices, structural cables, flexible fabrics, and other flexible structures.

Discovering Network Structure Beyond Communities

NASA Astrophysics Data System (ADS)

Nishikawa, Takashi; Motter, Adilson E.

2011-11-01

To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.

A clustering algorithm for determining community structure in complex networks

NASA Astrophysics Data System (ADS)

Jin, Hong; Yu, Wei; Li, ShiJun

2018-02-01

Clustering algorithms are attractive for the task of community detection in complex networks. DENCLUE is a representative density based clustering algorithm which has a firm mathematical basis and good clustering properties allowing for arbitrarily shaped clusters in high dimensional datasets. However, this method cannot be directly applied to community discovering due to its inability to deal with network data. Moreover, it requires a careful selection of the density parameter and the noise threshold. To solve these issues, a new community detection method is proposed in this paper. First, we use a spectral analysis technique to map the network data into a low dimensional Euclidean Space which can preserve node structural characteristics. Then, DENCLUE is applied to detect the communities in the network. A mathematical method named Sheather-Jones plug-in is chosen to select the density parameter which can describe the intrinsic clustering structure accurately. Moreover, every node on the network is meaningful so there were no noise nodes as a result the noise threshold can be ignored. We test our algorithm on both benchmark and real-life networks, and the results demonstrate the effectiveness of our algorithm over other popularity density based clustering algorithms adopted to community detection.

Improving resolution of dynamic communities in human brain networks through targeted node removal

PubMed Central

Turner, Benjamin O.; Miller, Michael B.; Carlson, Jean M.

2017-01-01

Current approaches to dynamic community detection in complex networks can fail to identify multi-scale community structure, or to resolve key features of community dynamics. We propose a targeted node removal technique to improve the resolution of community detection. Using synthetic oscillator networks with well-defined “ground truth” communities, we quantify the community detection performance of a common modularity maximization algorithm. We show that the performance of the algorithm on communities of a given size deteriorates when these communities are embedded in multi-scale networks with communities of different sizes, compared to the performance in a single-scale network. We demonstrate that targeted node removal during community detection improves performance on multi-scale networks, particularly when removing the most functionally cohesive nodes. Applying this approach to network neuroscience, we compare dynamic functional brain networks derived from fMRI data taken during both repetitive single-task and varied multi-task experiments. After the removal of regions in visual cortex, the most coherent functional brain area during the tasks, community detection is better able to resolve known functional brain systems into communities. In addition, node removal enables the algorithm to distinguish clear differences in brain network dynamics between these experiments, revealing task-switching behavior that was not identified with the visual regions present in the network. These results indicate that targeted node removal can improve spatial and temporal resolution in community detection, and they demonstrate a promising approach for comparison of network dynamics between neuroscientific data sets with different resolution parameters. PMID:29261662

Reusable tamper-indicating security seal

DOEpatents

Ryan, Michael J.

1983-01-01

The invention teaches means for detecting unauthorized tampering or substitutions of a device, and has particular utility when applied on a "seal" device used to secure a location or thing. The seal has a transparent body wall, and a first indicia, viz., a label identification is formed on the inside surface of this wall. Second and third indicia are formed on the outside surface of the transparent wall, and each of these indicia is transparent to allow the parallax angled viewing of the first indicia through these indicia. The second indicia is in the form of a broadly uniform pattern, viz, many small spaced dots; while the third indicia is in the form of easily memorized objects, such as human faces, made on a substrate by means of halftone printing. The substrate is lapped over the outside surface of the transparent wall. A thin cocoon of a transparent material, generally of the same material as the substrate such as plastic, is formed over the seal body and specifically over the transparent wall and the second and third indicia formed thereon. This cocoon is seamless and has walls of nonuniform thickness. Both the genuineness of the seal and whether anyone has attempted to compromise the seal can thus be visually determined upon inspection.

Ecological Networks and Community Attachment and Support Among Recently Resettled Refugees.

PubMed

Soller, Brian; Goodkind, Jessica R; Greene, R Neil; Browning, Christopher R; Shantzek, Cece

2018-03-25

Interventions aimed at enhancing mental health are increasingly centered around promoting community attachment and support. However, few have examined and tested the specific ecological factors that give rise to these key community processes. Drawing from insights from the ecological network perspective, we tested whether spatial and social overlap in routine activity settings (e.g., work, school, childcare) with fellow ethnic community members is associated with individuals' attachment to their ethnic communities and access to social resources embedded in their communities. Data on routine activity locations drawn from the Refugee Well-Being Project (based in a city in the Southwestern United States) were used to reconstruct the ecological networks of recently resettled refugee communities, which were two-mode networks that comprise individuals and their routine activity locations. Results indicated that respondents' community attachment and support increased with their ecological network extensity-which taps the extent to which respondents share routine activity locations with other community members. Our study highlights a key ecological process that potentially enhances individuals' ethnic community attachment that extends beyond residential neighborhoods. © Society for Community Research and Action 2018.

Network community structure and loop coefficient method

NASA Astrophysics Data System (ADS)

Vragović, I.; Louis, E.

2006-07-01

A modular structure, in which groups of tightly connected nodes could be resolved as separate entities, is a property that can be found in many complex networks. In this paper, we propose a algorithm for identifying communities in networks. It is based on a local measure, so-called loop coefficient that is a generalization of the clustering coefficient. Nodes with a large loop coefficient tend to be core inner community nodes, while other vertices are usually peripheral sites at the borders of communities. Our method gives satisfactory results for both artificial and real-world graphs, if they have a relatively pronounced modular structure. This type of algorithm could open a way of interpreting the role of nodes in communities in terms of the local loop coefficient, and could be used as a complement to other methods.

"Tampering to Death": A Fatal Codeine Intoxication Due to a Homemade Purification of a Medical Formulation.

PubMed

Fais, Paolo; Pigaiani, Nicola; Cecchetto, Giovanni; Montisci, Massimo; Gottardo, Rossella; Viel, Guido; Pascali, Jennifer Paola; Tagliaro, Franco

2017-11-01

Many homemade tamper processes of medical codeine formulations are available on selected "forums" on the Internet, where recreational codeine users claim to be able to purify codeine by removing additives, such as acetaminophen, to avoid or limit adverse effects. In this work, it is reported and discussed a fatal case of codeine intoxication. The findings of objects such as jars, filters, and tablets, and amounts of unknown liquid material at the death scene investigation suggested a fatal codeine intoxication after the tampering procedure called "cold water extraction." Toxicological results obtained from the analysis of both the nonbiological material and the body fluids of the decedent integrated with the information collected at the death scene investigation confirmed the above-mentioned hypothesis. This report underlines the importance of a tight interconnection between criminalistics and legal medicine to strengthen the identification of the cause of death and the reconstruction of the event. © 2017 American Academy of Forensic Sciences.

Dynamic robustness of knowledge collaboration network of open source product development community

NASA Astrophysics Data System (ADS)

Zhou, Hong-Li; Zhang, Xiao-Dong

2018-01-01

As an emergent innovative design style, open source product development communities are characterized by a self-organizing, mass collaborative, networked structure. The robustness of the community is critical to its performance. Using the complex network modeling method, the knowledge collaboration network of the community is formulated, and the robustness of the network is systematically and dynamically studied. The characteristics of the network along the development period determine that its robustness should be studied from three time stages: the start-up, development and mature stages of the network. Five kinds of user-loss pattern are designed, to assess the network's robustness under different situations in each of these three time stages. Two indexes - the largest connected component and the network efficiency - are used to evaluate the robustness of the community. The proposed approach is applied in an existing open source car design community. The results indicate that the knowledge collaboration networks show different levels of robustness in different stages and different user loss patterns. Such analysis can be applied to provide protection strategies for the key users involved in knowledge dissemination and knowledge contribution at different stages of the network, thereby promoting the sustainable and stable development of the open source community.

Social network analysis of stakeholder networks from two community-based obesity prevention interventions

PubMed Central

Nichols, Melanie; Korn, Ariella; Millar, Lynne; Marks, Jennifer; Sanigorski, Andrew; Pachucki, Mark; Swinburn, Boyd; Allender, Steven; Economos, Christina

2018-01-01

Introduction Studies of community-based obesity prevention interventions have hypothesized that stakeholder networks are a critical element of effective implementation. This paper presents a quantitative analysis of the interpersonal network structures within a sub-sample of stakeholders from two past successful childhood obesity prevention interventions. Methods Participants were recruited from the stakeholder groups (steering committees) of two completed community-based intervention studies, Romp & Chomp (R&C), Australia (2004-2008) and Shape Up Somerville (SUS), USA (2003-2005). Both studies demonstrated significant reductions of overweight and obesity among children. Members of the steering committees were asked to complete a retrospective social network questionnaire using a roster of other committee members and free recall. Each participant was asked to recall the people with whom they discussed issues related to childhood obesity throughout the intervention period, along with providing the closeness and level of influence of each relationship. Results Networks were reported by 13 participants from the SUS steering committee and 8 participants from the R&C steering committee. On average, participants nominated 16 contacts with whom they discussed issues related to childhood obesity through the intervention, with approximately half of the relationships described as ‘close’ and 30% as ‘influential’. The ‘discussion’ and ‘close’ networks had high clustering and reciprocity, with ties directed to other steering committee members, and to individuals external to the committee. In contrast, influential ties were more prominently directed internal to the steering committee, with higher network centralization, lower reciprocity and lower clustering. Discussion and conclusion Social network analysis provides a method to evaluate the ties within steering committees of community-based obesity prevention interventions. In this study, the network

The optimal community detection of software based on complex networks

NASA Astrophysics Data System (ADS)

Huang, Guoyan; Zhang, Peng; Zhang, Bing; Yin, Tengteng; Ren, Jiadong

2016-02-01

The community structure is important for software in terms of understanding the design patterns, controlling the development and the maintenance process. In order to detect the optimal community structure in the software network, a method Optimal Partition Software Network (OPSN) is proposed based on the dependency relationship among the software functions. First, by analyzing the information of multiple execution traces of one software, we construct Software Execution Dependency Network (SEDN). Second, based on the relationship among the function nodes in the network, we define Fault Accumulation (FA) to measure the importance of the function node and sort the nodes with measure results. Third, we select the top K(K=1,2,…) nodes as the core of the primal communities (only exist one core node). By comparing the dependency relationships between each node and the K communities, we put the node into the existing community which has the most close relationship. Finally, we calculate the modularity with different initial K to obtain the optimal division. With experiments, the method OPSN is verified to be efficient to detect the optimal community in various softwares.

Modeling information diffusion in time-varying community networks

NASA Astrophysics Data System (ADS)

Cui, Xuelian; Zhao, Narisa

2017-12-01

Social networks are rarely static, and they typically have time-varying network topologies. A great number of studies have modeled temporal networks and explored social contagion processes within these models; however, few of these studies have considered community structure variations. In this paper, we present a study of how the time-varying property of a modular structure influences the information dissemination. First, we propose a continuous-time Markov model of information diffusion where two parameters, mobility rate and community attractiveness, are introduced to address the time-varying nature of the community structure. The basic reproduction number is derived, and the accuracy of this model is evaluated by comparing the simulation and theoretical results. Furthermore, numerical results illustrate that generally both the mobility rate and community attractiveness significantly promote the information diffusion process, especially in the initial outbreak stage. Moreover, the strength of this promotion effect is much stronger when the modularity is higher. Counterintuitively, it is found that when all communities have the same attractiveness, social mobility no longer accelerates the diffusion process. In addition, we show that the local spreading in the advantage group has been greatly enhanced due to the agglomeration effect caused by the social mobility and community attractiveness difference, which thus increases the global spreading.

Reversible Data Hiding in FTIR Microspectroscopy Images with Tamper Indication and Payload Error Correction

PubMed Central

Seppänen, Tapio

2017-01-01

Fourier transform infrared (FTIR) microspectroscopy images contain information from the whole infrared spectrum used for microspectroscopic analyses. In combination with the FTIR image, visible light images are used to depict the area from which the FTIR spectral image was sampled. These two images are traditionally acquired as separate files. This paper proposes a histogram shifting-based data hiding technique to embed visible light images in FTIR spectral images producing single entities. The primary objective is to improve data management efficiency. Secondary objectives are confidentiality, availability, and reliability. Since the integrity of biomedical data is vital, the proposed method applies reversible data hiding. After extraction of the embedded data, the FTIR image is reversed to its original state. Furthermore, the proposed method applies authentication tags generated with keyed Hash-Based Message Authentication Codes (HMAC) to detect tampered or corrupted areas of FTIR images. The experimental results show that the FTIR spectral images carrying the payload maintain good perceptual fidelity and the payload can be reliably recovered even after bit flipping or cropping attacks. It has been also shown that extraction successfully removes all modifications caused by the payload. Finally, authentication tags successfully indicated tampered FTIR image areas. PMID:29259987

Community Size Effects on Epidemic Spreading in Multiplex Social Networks.

PubMed

Liu, Ting; Li, Ping; Chen, Yan; Zhang, Jie

2016-01-01

The dynamical process of epidemic spreading has drawn much attention of the complex network community. In the network paradigm, diseases spread from one person to another through the social ties amongst the population. There are a variety of factors that govern the processes of disease spreading on the networks. A common but not negligible factor is people's reaction to the outbreak of epidemics. Such reaction can be related information dissemination or self-protection. In this work, we explore the interactions between disease spreading and population response in terms of information diffusion and individuals' alertness. We model the system by mapping multiplex networks into two-layer networks and incorporating individuals' risk awareness, on the assumption that their response to the disease spreading depends on the size of the community they belong to. By comparing the final incidence of diseases in multiplex networks, we find that there is considerable mitigation of diseases spreading for full phase of spreading speed when individuals' protection responses are introduced. Interestingly, the degree of community overlap between the two layers is found to be critical factor that affects the final incidence. We also analyze the consequences of the epidemic incidence in communities with different sizes and the impacts of community overlap between two layers. Specifically, as the diseases information makes individuals alert and take measures to prevent the diseases, the effective protection is more striking in small community. These phenomena can be explained by the multiplexity of the networked system and the competition between two spreading processes.

Community Size Effects on Epidemic Spreading in Multiplex Social Networks

PubMed Central

Liu, Ting; Li, Ping; Chen, Yan; Zhang, Jie

2016-01-01

The dynamical process of epidemic spreading has drawn much attention of the complex network community. In the network paradigm, diseases spread from one person to another through the social ties amongst the population. There are a variety of factors that govern the processes of disease spreading on the networks. A common but not negligible factor is people’s reaction to the outbreak of epidemics. Such reaction can be related information dissemination or self-protection. In this work, we explore the interactions between disease spreading and population response in terms of information diffusion and individuals’ alertness. We model the system by mapping multiplex networks into two-layer networks and incorporating individuals’ risk awareness, on the assumption that their response to the disease spreading depends on the size of the community they belong to. By comparing the final incidence of diseases in multiplex networks, we find that there is considerable mitigation of diseases spreading for full phase of spreading speed when individuals’ protection responses are introduced. Interestingly, the degree of community overlap between the two layers is found to be critical factor that affects the final incidence. We also analyze the consequences of the epidemic incidence in communities with different sizes and the impacts of community overlap between two layers. Specifically, as the diseases information makes individuals alert and take measures to prevent the diseases, the effective protection is more striking in small community. These phenomena can be explained by the multiplexity of the networked system and the competition between two spreading processes. PMID:27007112

Identifying and characterizing key nodes among communities based on electrical-circuit networks.

PubMed

Zhu, Fenghui; Wang, Wenxu; Di, Zengru; Fan, Ying

2014-01-01

Complex networks with community structures are ubiquitous in the real world. Despite many approaches developed for detecting communities, we continue to lack tools for identifying overlapping and bridging nodes that play crucial roles in the interactions and communications among communities in complex networks. Here we develop an algorithm based on the local flow conservation to effectively and efficiently identify and distinguish the two types of nodes. Our method is applicable in both undirected and directed networks without a priori knowledge of the community structure. Our method bypasses the extremely challenging problem of partitioning communities in the presence of overlapping nodes that may belong to multiple communities. Due to the fact that overlapping and bridging nodes are of paramount importance in maintaining the function of many social and biological networks, our tools open new avenues towards understanding and controlling real complex networks with communities accompanied with the key nodes.

a New Dynamic Community Model for Social Networks

NASA Astrophysics Data System (ADS)

Lu, Zhe-Ming; Wu, Zhen; Guo, Shi-Ze; Chen, Zhe; Song, Guang-Hua

2014-09-01

In this paper, based on the phenomenon that individuals join into and jump from the organizations in the society, we propose a dynamic community model to construct social networks. Two parameters are adopted in our model, one is the communication rate Pa that denotes the connection strength in the organization and the other is the turnover rate Pb, that stands for the frequency of jumping among the organizations. Based on simulations, we analyze not only the degree distribution, the clustering coefficient, the average distance and the network diameter but also the group distribution which is closely related to their community structure. Moreover, we discover that the networks generated by the proposed model possess the small-world property and can well reproduce the networks of social contacts.

Utilising eduroam[TM] Architecture in Building Wireless Community Networks

ERIC Educational Resources Information Center

Huhtanen, Karri; Vatiainen, Heikki; Keski-Kasari, Sami; Harju, Jarmo

2008-01-01

Purpose: eduroam[TM] has already been proved to be a scalable, secure and feasible way for universities and research institutions to connect their wireless networks into a WLAN roaming community, but the advantages of eduroam[TM] have not yet been fully discovered in the wireless community networks aimed at regular consumers. This aim of this…

An Outbreak of Norovirus Infections Among Lunch Customers at a Restaurant, Tampere, Finland, 2015.

PubMed

Vo, Thuan Huu; Okasha, Omar; Al-Hello, Haider; Polkowska, Aleksandra; Räsänen, Sirpa; Bojang, Merja; Nuorti, J Pekka; Jalava, Katri

2016-09-01

On January 29, 2015, the city of Tampere environmental health officers were informed of a possible foodborne outbreak among customers who had eaten lunch in restaurant X. Employees of electric companies A and B had a sudden onset of gastrointestinal symptoms. We conducted a retrospective cohort study to identify the vehicle, source, and causative agent of the outbreak. A case was defined as an employee of companies A or B with diarrhea and/or vomiting who ate lunch at Restaurant X on January 26, 2015. All employees of the companies attending the implicated lunch were invited to participate in the cohort study. Environmental investigation was conducted. Twenty-one responders were included in statistical analysis, of which 11 met with the case definition. Of the 15 food items consumed by participants, four food items were associated with gastroenteritis. Of four kitchen staff, three tested positive for norovirus GIP7, the strain was found earlier in the community. No patient samples were obtained. Level of hygiene in the kitchen was inadequate. Infected kitchen staff probably transmitted norovirus by inadequate hygiene practices. No new cases associated with Restaurant X were reported after the hygiene practices were improved.

Evaluating the Effectiveness of Community-Based Dementia Care Networks: The Dementia Care Networks' Study

ERIC Educational Resources Information Center

Lemieux-Charles, Louis; Chambers, Larry W.; Cockerill, Rhonda; Jaglal, Susan; Brazil, Kevin; Cohen, Carole; LeClair, Ken; Dalziel, Bill; Schulman, Barbara

2005-01-01

Purpose: The Dementia Care Networks' Study examined the effectiveness of four community-based, not-for-profit dementia networks. The study involved assessing the relationship between the types of administrative and service-delivery exchanges that occurred among the networked agencies and the network members' perception of the effectiveness of…

A density-based clustering model for community detection in complex networks

NASA Astrophysics Data System (ADS)

Zhao, Xiang; Li, Yantao; Qu, Zehui

2018-04-01

Network clustering (or graph partitioning) is an important technique for uncovering the underlying community structures in complex networks, which has been widely applied in various fields including astronomy, bioinformatics, sociology, and bibliometric. In this paper, we propose a density-based clustering model for community detection in complex networks (DCCN). The key idea is to find group centers with a higher density than their neighbors and a relatively large integrated-distance from nodes with higher density. The experimental results indicate that our approach is efficient and effective for community detection of complex networks.

Detecting and evaluating communities in complex human and biological networks

NASA Astrophysics Data System (ADS)

Morrison, Greg; Mahadevan, L.

2012-02-01

We develop a simple method for detecting the community structure in a network can by utilizing a measure of closeness between nodes. This approach readily leads to a method of coarse graining the network, which allows the detection of the natural hierarchy (or hierarchies) of community structure without appealing to an unknown resolution parameter. The closeness measure can also be used to evaluate the robustness of an individual node's assignment to its community (rather than evaluating only the quality of the global structure). Each of these methods in community detection and evaluation are illustrated using a variety of real world networks of either biological or sociological importance and illustrate the power and flexibility of the approach.

Community detection in networks with unequal groups.

PubMed

Zhang, Pan; Moore, Cristopher; Newman, M E J

2016-01-01

Recently, a phase transition has been discovered in the network community detection problem below which no algorithm can tell which nodes belong to which communities with success any better than a random guess. This result has, however, so far been limited to the case where the communities have the same size or the same average degree. Here we consider the case where the sizes or average degrees differ. This asymmetry allows us to assign nodes to communities with better-than-random success by examining their local neighborhoods. Using the cavity method, we show that this removes the detectability transition completely for networks with four groups or fewer, while for more than four groups the transition persists up to a critical amount of asymmetry but not beyond. The critical point in the latter case coincides with the point at which local information percolates, causing a global transition from a less-accurate solution to a more-accurate one.

Purpose-Driven Communities in Multiplex Networks: Thresholding User-Engaged Layer Aggregation

DTIC Science & Technology

2016-06-01

dark networks is a non-trivial yet useful task. Because terrorists work hard to hide their relationships/network, analysts have an incomplete picture...them identify meaningful terrorist communities. This thesis introduces a general-purpose algorithm for community detection in multiplex dark networks...aggregation, dark networks, conductance, cluster adequacy, mod- ularity, Louvain method, shortest path interdiction 15. NUMBER OF PAGES 155 16. PRICE CODE

Improved community model for social networks based on social mobility

NASA Astrophysics Data System (ADS)

Lu, Zhe-Ming; Wu, Zhen; Luo, Hao; Wang, Hao-Xian

2015-07-01

This paper proposes an improved community model for social networks based on social mobility. The relationship between the group distribution and the community size is investigated in terms of communication rate and turnover rate. The degree distributions, clustering coefficients, average distances and diameters of networks are analyzed. Experimental results demonstrate that the proposed model possesses the small-world property and can reproduce social networks effectively and efficiently.

Liking and hyperlinking: Community detection in online child sexual exploitation networks.

PubMed

Westlake, Bryce G; Bouchard, Martin

2016-09-01

The online sexual exploitation of children is facilitated by websites that form virtual communities, via hyperlinks, to distribute images, videos, and other material. However, how these communities form, are structured, and evolve over time is unknown. Collected using a custom-designed webcrawler, we begin from known child sexual exploitation (CE) seed websites and follow hyperlinks to connected, related, websites. Using a repeated measure design we analyze 10 networks of 300 + websites each - over 4.8 million unique webpages in total, over a period of 60 weeks. Community detection techniques reveal that CE-related networks were dominated by two large communities hosting varied material -not necessarily matching the seed website. Community stability, over 60 weeks, varied across networks. Reciprocity in hyperlinking between community members was substantially higher than within the full network, however, websites were not more likely to connect to homogeneous-content websites. Copyright © 2016 Elsevier Inc. All rights reserved.

Developing an inter-organizational community-based health network: an Australian investigation.

PubMed

Short, Alison; Phillips, Rebecca; Nugus, Peter; Dugdale, Paul; Greenfield, David

2015-12-01

Networks in health care typically involve services delivered by a defined set of organizations. However, networked associations between the healthcare system and consumers or consumer organizations tend to be open, fragmented and are fraught with difficulties. Understanding the role and activities of consumers and consumer groups in a formally initiated inter-organizational health network, and the impacts of the network, is a timely endeavour. This study addresses this aim in three ways. First, the Unbounded Network Inter-organizational Collaborative Impact Model, a purpose-designed framework developed from existing literature, is used to investigate the process and products of inter-organizational network development. Second, the impact of a network artefact is explored. Third, the lessons learned in inter-organizational network development are considered. Data collection methods were: 16 h of ethnographic observation; 10 h of document analysis; six interviews with key informants and a survey (n = 60). Findings suggested that in developing the network, members used common aims, inter-professional collaboration, the power and trust engendered by their participation, and their leadership and management structures in a positive manner. These elements and activities underpinned the inter-organizational network to collaboratively produce the Health Expo network artefact. This event brought together healthcare providers, community groups and consumers to share information. The Health Expo demonstrated and reinforced inter-organizational working and community outreach, providing consumers with community-based information and linkages. Support and resources need to be offered for developing community inter-organizational networks, thereby building consumer capacity for self-management in the community. © The Author (2014). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Community Structure of a Bank-Firm Credit Network in Japan

NASA Astrophysics Data System (ADS)

Iyetomi, Hiroshi; Matsuura, Yuki

2014-03-01

We study temporal change of community structure in a Japanese credit network formed by banks and listed firms through their financial relations over the last 30 years. The credit connectedness is regarded as a potenital source of systemic risk. Our network is a bipartite graph consisting of two species of nodes connected with bidirectional links. The direction of links is identified with that of risk flows and their weights are relative credit/loan with respect to the targets. In a partial credit network obtained only with the links pointing from firms toward banks, the city banks forms one major community in most of the time period to share risk when firms go wrong. On the other hand, a partial network only with the links from banks toward firms is decomposed into communities of similar size each of which has its own city bank, reflecting the main-bank system in Japan. Finally we take overlapping parts of the two community sets to find cores of the risk concentration in the credit network. This work was supported by JSPS KAKENHI Grant Number 22300080.

Followers are not enough: a multifaceted approach to community detection in online social networks.

PubMed

Darmon, David; Omodei, Elisa; Garland, Joshua

2015-01-01

In online social media networks, individuals often have hundreds or even thousands of connections, which link these users not only to friends, associates, and colleagues, but also to news outlets, celebrities, and organizations. In these complex social networks, a 'community' as studied in the social network literature, can have very different meaning depending on the property of the network under study. Taking into account the multifaceted nature of these networks, we claim that community detection in online social networks should also be multifaceted in order to capture all of the different and valuable viewpoints of 'community.' In this paper we focus on three types of communities beyond follower-based structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that interesting insights can be obtained about the complex community structure present in social networks by studying when and how these four community types give rise to similar as well as completely distinct community structure.

Opinion diversity and community formation in adaptive networks

NASA Astrophysics Data System (ADS)

Yu, Y.; Xiao, G.; Li, G.; Tay, W. P.; Teoh, H. F.

2017-10-01

It is interesting and of significant importance to investigate how network structures co-evolve with opinions. In this article, we show that, a simple model integrating consensus formation, link rewiring, and opinion change allows complex system dynamics to emerge, driving the system into a dynamic equilibrium with the co-existence of diversified opinions. Specifically, similar opinion holders may form into communities yet with no strict community consensus; and rather than being separated into disconnected communities, different communities are connected by a non-trivial proportion of inter-community links. More importantly, we show that the complex dynamics may lead to different numbers of communities at the steady state with a given tolerance between different opinion holders. We construct a framework for theoretically analyzing the co-evolution process. Theoretical analysis and extensive simulation results reveal some useful insights into the complex co-evolution process, including the formation of dynamic equilibrium, the transition between different steady states with different numbers of communities, and the dynamics between opinion distribution and network modularity.

Home-School Links: Networking the Learning Community.

ERIC Educational Resources Information Center

1996

The topic of networking the learning community with home-school links is addressed in four papers: "Internet Access via School: Expectations of Students and Parents" (Roy Crotty); "The School Library as Community Information Gateway" (Megan Perry); "Rural Access to the Internet" (Ken Eustace); and "NetDay '96:…

Community detection in complex networks by using membrane algorithm

NASA Astrophysics Data System (ADS)

Liu, Chuang; Fan, Linan; Liu, Zhou; Dai, Xiang; Xu, Jiamei; Chang, Baoren

Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

Extracting Communities from Complex Networks by the k-Dense Method

NASA Astrophysics Data System (ADS)

Saito, Kazumi; Yamada, Takeshi; Kazama, Kazuhiro

To understand the structural and functional properties of large-scale complex networks, it is crucial to efficiently extract a set of cohesive subnetworks as communities. There have been proposed several such community extraction methods in the literature, including the classical k-core decomposition method and, more recently, the k-clique based community extraction method. The k-core method, although computationally efficient, is often not powerful enough for uncovering a detailed community structure and it produces only coarse-grained and loosely connected communities. The k-clique method, on the other hand, can extract fine-grained and tightly connected communities but requires a substantial amount of computational load for large-scale complex networks. In this paper, we present a new notion of a subnetwork called k-dense, and propose an efficient algorithm for extracting k-dense communities. We applied our method to the three different types of networks assembled from real data, namely, from blog trackbacks, word associations and Wikipedia references, and demonstrated that the k-dense method could extract communities almost as efficiently as the k-core method, while the qualities of the extracted communities are comparable to those obtained by the k-clique method.

Community detection in complex networks using link prediction

NASA Astrophysics Data System (ADS)

Cheng, Hui-Min; Ning, Yi-Zi; Yin, Zhao; Yan, Chao; Liu, Xin; Zhang, Zhong-Yuan

2018-01-01

Community detection and link prediction are both of great significance in network analysis, which provide very valuable insights into topological structures of the network from different perspectives. In this paper, we propose a novel community detection algorithm with inclusion of link prediction, motivated by the question whether link prediction can be devoted to improving the accuracy of community partition. For link prediction, we propose two novel indices to compute the similarity between each pair of nodes, one of which aims to add missing links, and the other tries to remove spurious edges. Extensive experiments are conducted on benchmark data sets, and the results of our proposed algorithm are compared with two classes of baselines. In conclusion, our proposed algorithm is competitive, revealing that link prediction does improve the precision of community detection.

Game theory and extremal optimization for community detection in complex dynamic networks.

PubMed

Lung, Rodica Ioana; Chira, Camelia; Andreica, Anca

2014-01-01

The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach.

Followers Are Not Enough: A Multifaceted Approach to Community Detection in Online Social Networks

PubMed Central

2015-01-01

In online social media networks, individuals often have hundreds or even thousands of connections, which link these users not only to friends, associates, and colleagues, but also to news outlets, celebrities, and organizations. In these complex social networks, a ‘community’ as studied in the social network literature, can have very different meaning depending on the property of the network under study. Taking into account the multifaceted nature of these networks, we claim that community detection in online social networks should also be multifaceted in order to capture all of the different and valuable viewpoints of ‘community.’ In this paper we focus on three types of communities beyond follower-based structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that interesting insights can be obtained about the complex community structure present in social networks by studying when and how these four community types give rise to similar as well as completely distinct community structure. PMID:26267868

A local immunization strategy for networks with overlapping community structure

NASA Astrophysics Data System (ADS)

Taghavian, Fatemeh; Salehi, Mostafa; Teimouri, Mehdi

2017-02-01

Since full coverage treatment is not feasible due to limited resources, we need to utilize an immunization strategy to effectively distribute the available vaccines. On the other hand, the structure of contact network among people has a significant impact on epidemics of infectious diseases (such as SARS and influenza) in a population. Therefore, network-based immunization strategies aim to reduce the spreading rate by removing the vaccinated nodes from contact network. Such strategies try to identify more important nodes in epidemics spreading over a network. In this paper, we address the effect of overlapping nodes among communities on epidemics spreading. The proposed strategy is an optimized random-walk based selection of these nodes. The whole process is local, i.e. it requires contact network information in the level of nodes. Thus, it is applicable to large-scale and unknown networks in which the global methods usually are unrealizable. Our simulation results on different synthetic and real networks show that the proposed method outperforms the existing local methods in most cases. In particular, for networks with strong community structures, high overlapping membership of nodes or small size communities, the proposed method shows better performance.

"Old Age and Loneliness: Cross-Sectional and Longitudinal Analyses in the Tampere Longitudinal Study on Aging"

ERIC Educational Resources Information Center

Jylha, Marja

2004-01-01

The purpose of this study was to examine whether older age is associated with increasing loneliness in people aged 60 and over. Data came from TamELSA, a population-based prospective longitudinal study in Tampere, Finland. The followup time was 20 years. Loneliness was measured by a single question--"Do you feel lonely?"--with the…

Tamper asymmetry and its effect on transmission for x-ray driven opacity simulations

DOE PAGES

Morris, H. E.; Tregillis, I. L.; Hoffman, N. M.; ...

2017-08-01

This paper reports on synthetic transmission results from Lasnex [1] radiation-hydrodynamics simulations of opacity experiments carried out at Sandia National Laboratories' recently upgraded ZR facility. The focus is on experiments utilizing disk targets composed of a half-moon Fe/Mg mixture tamped on either end with 10- m CH and an additional 35- m beryllium tamper accessory on the end facing the spectrometer. Five x-ray sources with peak power ranging from 10 to 24 TW were used in the simulations to heat and backlight the opacity target. The dominant effect is that the beryllium behind the Fe/Mg mixture is denser and moremore » opaque than the beryllium unshielded by metal during the times of greatest importance for the transmission measurement for all drives. This causes the simulated transmission to be lower than expected, and this is most pronounced for the case using the lowest drive power. While beryllium has a low opacity, its areal density is sufficiently high such that the expected reduction of the measured transmission is significant. This situation leads to an overestimate of iron opacity by 10-215% for a photon energy range of 975- 1775 eV for the 10-TW case. It is shown that if the tamper conditions are known, the transmission through each component of the target can be calculated and the resulting opacity can be corrected.« less

Discovering Link Communities in Complex Networks by an Integer Programming Model and a Genetic Algorithm

PubMed Central

Li, Zhenping; Zhang, Xiang-Sun; Wang, Rui-Sheng; Liu, Hongwei; Zhang, Shihua

2013-01-01

Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks. PMID:24386268

Interest communities and flow roles in directed networks: the Twitter network of the UK riots

PubMed Central

Beguerisse-Díaz, Mariano; Garduño-Hernández, Guillermo; Vangelov, Borislav; Yaliraki, Sophia N.; Barahona, Mauricio

2014-01-01

Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e. groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer and topic. The study of flows also allows us to generate an interest distance, which affords a personalized view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterized by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks. PMID:25297320

Multi-Relational Characterization of Dynamic Social Network Communities

NASA Astrophysics Data System (ADS)

Lin, Yu-Ru; Sundaram, Hari; Kelliher, Aisling

The emergence of the mediated social web - a distributed network of participants creating rich media content and engaging in interactive conversations through Internet-based communication technologies - has contributed to the evolution of powerful social, economic and cultural change. Online social network sites and blogs, such as Facebook, Twitter, Flickr and LiveJournal, thrive due to their fundamental sense of "community". The growth of online communities offers both opportunities and challenges for researchers and practitioners. Participation in online communities has been observed to influence people's behavior in diverse ways ranging from financial decision-making to political choices, suggesting the rich potential for diverse applications. However, although studies on the social web have been extensive, discovering communities from online social media remains challenging, due to the interdisciplinary nature of this subject. In this article, we present our recent work on characterization of communities in online social media using computational approaches grounded on the observations from social science.

Social Networks as a Political Resource: Some Insights Drawn from the Community Organizational and Community Action Experiences.

ERIC Educational Resources Information Center

Rosenbaum, Allan

The development and functioning of urban social networks in highly politicized environments--particularly, the neighborhood based community organization, political coalition building of urban mayors, and community action programs--suggest implications for building locally based educational reform capacity through network development. Community…

Emergence of bursts and communities in evolving weighted networks.

PubMed

Jo, Hang-Hyun; Pan, Raj Kumar; Kaski, Kimmo

2011-01-01

Understanding the patterns of human dynamics and social interaction and the way they lead to the formation of an organized and functional society are important issues especially for techno-social development. Addressing these issues of social networks has recently become possible through large scale data analysis of mobile phone call records, which has revealed the existence of modular or community structure with many links between nodes of the same community and relatively few links between nodes of different communities. The weights of links, e.g., the number of calls between two users, and the network topology are found correlated such that intra-community links are stronger compared to the weak inter-community links. This feature is known as Granovetter's "The strength of weak ties" hypothesis. In addition to this inhomogeneous community structure, the temporal patterns of human dynamics turn out to be inhomogeneous or bursty, characterized by the heavy tailed distribution of time interval between two consecutive events, i.e., inter-event time. In this paper, we study how the community structure and the bursty dynamics emerge together in a simple evolving weighted network model. The principal mechanisms behind these patterns are social interaction by cyclic closure, i.e., links to friends of friends and the focal closure, links to individuals sharing similar attributes or interests, and human dynamics by task handling process. These three mechanisms have been implemented as a network model with local attachment, global attachment, and priority-based queuing processes. By comprehensive numerical simulations we show that the interplay of these mechanisms leads to the emergence of heavy tailed inter-event time distribution and the evolution of Granovetter-type community structure. Moreover, the numerical results are found to be in qualitative agreement with empirical analysis results from mobile phone call dataset.

Energy Spectral Behaviors of Communication Networks of Open-Source Communities

PubMed Central

Yang, Jianmei; Yang, Huijie; Liao, Hao; Wang, Jiangtao; Zeng, Jinqun

2015-01-01

Large-scale online collaborative production activities in open-source communities must be accompanied by large-scale communication activities. Nowadays, the production activities of open-source communities, especially their communication activities, have been more and more concerned. Take CodePlex C # community for example, this paper constructs the complex network models of 12 periods of communication structures of the community based on real data; then discusses the basic concepts of quantum mapping of complex networks, and points out that the purpose of the mapping is to study the structures of complex networks according to the idea of quantum mechanism in studying the structures of large molecules; finally, according to this idea, analyzes and compares the fractal features of the spectra in different quantum mappings of the networks, and concludes that there are multiple self-similarity and criticality in the communication structures of the community. In addition, this paper discusses the insights and application conditions of different quantum mappings in revealing the characteristics of the structures. The proposed quantum mapping method can also be applied to the structural studies of other large-scale organizations. PMID:26047331

Community partnerships in healthy eating and lifestyle promotion: A network analysis.

PubMed

An, Ruopeng; Loehmer, Emily; Khan, Naiman; Scott, Marci K; Rindfleisch, Kimbirly; McCaffrey, Jennifer

2017-06-01

Promoting healthy eating and lifestyles among populations with limited resources is a complex undertaking that often requires strong partnerships between various agencies. In local communities, these agencies are typically located in different areas, serve diverse subgroups, and operate distinct programs, limiting their communication and interactions with each other. This study assessed the network of agencies in local communities that promote healthy eating and lifestyles among populations with limited resources. Network surveys were administered in 2016 among 89 agencies located in 4 rural counties in Michigan that served limited-resource audiences. The agencies were categorized into 8 types: K-12 schools, early childhood centers, emergency food providers, health-related agencies, social resource centers, low-income/subsidized housing complexes, continuing education organizations, and others. Network analysis was conducted to examine 4 network structures-communication, funding, cooperation, and collaboration networks between agencies within each county. Agencies had a moderate level of cooperation, but were only loosely connected in the other 3 networks, indicated by low network density. Agencies in a network were decentralized rather than centralized around a few influential agencies, indicated by low centralization. There was evidence regarding homophily in a network, indicated by some significant correlations within agencies of the same type. Agencies connected in any one network were considerably more likely to be connected in all the other networks as well. In conclusion, promoting healthy eating and lifestyles among populations with limited resources warrants strong partnership between agencies in communities. Network analysis serves as a useful tool to evaluate community partnerships and facilitate coalition building.

Money circulation networks reveal emerging geographical communities

NASA Astrophysics Data System (ADS)

Brockmann, D.; Theis, F.; David, V.

2008-03-01

Geographical communities and their boundaries are key determinants of various spatially extended dynamical phenomena. Examples are migration dynamics of species, the spread of infectious diseases, bioinvasive processes, and the spatial evolution of language. We address the question to what extend multiscale human transportation networks encode geographical community structures, how they differ from geopolitical classifications, whether they are spatially coherent, and analyse their structure as a function of length scale. Our analysis is based on a proxy network for human transportation obtained from the geographic circulation of more than 10 million dollar bills in the United States recorded at the bill tracking website www.wheresgeorge.com. The data extends that of a previous study (Brockmann et al., Nature 2006) on the discovery of scaling laws of human travel by an order of magnitude and permits an approach to multiscale human transportation from a network perspective.

Consensus-based methodology for detection communities in multilayered networks

NASA Astrophysics Data System (ADS)

Karimi-Majd, Amir-Mohsen; Fathian, Mohammad; Makrehchi, Masoud

2018-03-01

Finding groups of network users who are densely related with each other has emerged as an interesting problem in the area of social network analysis. These groups or so-called communities would be hidden behind the behavior of users. Most studies assume that such behavior could be understood by focusing on user interfaces, their behavioral attributes or a combination of these network layers (i.e., interfaces with their attributes). They also assume that all network layers refer to the same behavior. However, in real-life networks, users' behavior in one layer may differ from their behavior in another one. In order to cope with these issues, this article proposes a consensus-based community detection approach (CBC). CBC finds communities among nodes at each layer, in parallel. Then, the results of layers should be aggregated using a consensus clustering method. This means that different behavior could be detected and used in the analysis. As for other significant advantages, the methodology would be able to handle missing values. Three experiments on real-life and computer-generated datasets have been conducted in order to evaluate the performance of CBC. The results indicate superiority and stability of CBC in comparison to other approaches.

Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

PubMed Central

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms. PMID:24723806

Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks.

PubMed

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

Friendship Concept and Community Network Structure among Elementary School and University Students.

PubMed

Hernández-Hernández, Ana María; Viga-de Alva, Dolores; Huerta-Quintanilla, Rodrigo; Canto-Lugo, Efrain; Laviada-Molina, Hugo; Molina-Segui, Fernanda

2016-01-01

We use complex network theory to study the differences between the friendship concepts in elementary school and university students. Four friendship networks were identified from surveys. Three of these networks are from elementary schools; two are located in the rural area of Yucatán and the other is in the urban area of Mérida, Yucatán. We analyzed the structure and the communities of these friendship networks and found significant differences among those at the elementary schools compared with those at the university. In elementary schools, the students make friends mainly in the same classroom, but there are also links among different classrooms because of the presence of siblings and relatives in the schools. These kinds of links (sibling-friend or relative-friend) are called, in this work, "mixed links". The classification of the communities is based on their similarity with the classroom composition. If the community is composed principally of students in different classrooms, the community is classified as heterogeneous. These kinds of communities appear in the elementary school friendship networks mainly because of the presence of relatives and siblings. Once the links between siblings and relatives are removed, the communities resembled the classroom composition. On the other hand, the university students are more selective in choosing friends and therefore, even when they have friends in the same classroom, those communities are quite different to the classroom composition. Also, in the university network, we found heterogeneous communities even when the presence of sibling and relatives is negligible. These differences made up a topological structure quite different at different academic levels. We also found differences in the network characteristics. Once these differences are understood, the topological structure of the friendship network and the communities shaped in an elementary school could be predicted if we know the total number of students

A Comparative Analysis of Community Detection Algorithms on Artificial Networks

PubMed Central

Yang, Zhao; Algesheimer, René; Tessone, Claudio J.

2016-01-01

Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms’ computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm’s predicting power and the effective computing time. PMID:27476470

A Community Network of 100 Black Carbon Sensors

NASA Astrophysics Data System (ADS)

Preble, C.; Kirchstetter, T.; Caubel, J.; Cados, T.; Keeling, C.; Chang, S.

2017-12-01

We developed a low-cost black carbon sensor, field tested its performance, and then built and deployed a network of 100 sensors in West Oakland, California. We operated the network for 100 days beginning mid-May 2017 to measure spatially resolved black carbon concentrations throughout the community. West Oakland is a San Francisco Bay Area mixed residential and industrial community that is adjacent to regional port and rail yard facilities and surrounded by major freeways. As such, the community is affected by diesel particulate matter emissions from heavy-duty diesel trucks, locomotives, and ships associated with freight movement. In partnership with Environmental Defense Fund, the Bay Area Air Quality Management District, and the West Oakland Environmental Indicators Project, we deployed the black carbon monitoring network outside of residences and business, along truck routes and arterial streets, and at upwind locations. The sensor employs the filter-based light transmission method to measure black carbon and has good precision and correspondence with current commercial black carbon instruments. Throughout the 100-day period, each of the 100 sensors transmitted data via a cellular network. A MySQL database was built to receive and manage the data in real-time. The database included diagnostic features to monitor each sensor's operational status and facilitate the maintenance of the network. Spatial and temporal patterns in black carbon concentrations will be presented, including patterns around industrial facilities, freeways, and truck routes, as well as the relationship between neighborhood concentrations and the BAAQMD's monitoring site. Lessons learned during this first of its kind black carbon monitoring network will also be shared.

Chemeketa Community College Telecommunications Network: A Proposal to the Chemeketa Community College Board.

ERIC Educational Resources Information Center

Rude, John C.

Developed by the Telecommunications Committee of Chemeketa Community College (CCC), this report recommends the development of a telecommunications network to supply instruction by television, computers and telephone to homes, businesses, and CCC branch campuses. The report begins by stressing the advantages of a telecommunications network and its…

Rock Hill Business, Education, and Community Online Network.

ERIC Educational Resources Information Center

Broyles, Alan

The Business, Education & Community On-line Network (BEACON) is designed to support development and implementation of demonstration applications operating in an asynchronous transfer mode (ATM) fiber optic network environment. Initial origination and destination sites include high schools and universities around Rock Hill (South Carolina). The…

Network Community Detection based on the Physarum-inspired Computational Framework.

PubMed

Gao, Chao; Liang, Mingxin; Li, Xianghua; Zhang, Zili; Wang, Zhen; Zhou, Zhili

2016-12-13

Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, which is a large amoeba-like cell consisting of a dendritic network of tube-like pseudopodia, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost. Moreover, a computational complexity analysis verifies the scalability of our framework.

Tamper asymmetry and its effect on transmission for x-ray driven opacity simulations

NASA Astrophysics Data System (ADS)

Morris, H. E.; Tregillis, I. L.; Hoffman, N. M.; Sherrill, M. E.; Fontes, C. J.; Marshall, A. J.; Urbatsch, T. J.; Bradley, P. A.

2017-09-01

This paper reports on synthetic transmission results from Lasnex [Zimmerman and Kruer, Comments Plasma Phys. 2, 51 (1975)] radiation-hydrodynamics simulations of opacity experiments carried out at Sandia National Laboratories' recently upgraded ZR facility. The focus is on experiments utilizing disk targets composed of a half-moon Fe/Mg mixture tamped on either end with 10-μm CH and an additional 35-μm beryllium tamper accessory on the end facing the spectrometer. Five x-ray sources with peak power ranging from 10 to 24 TW were used in the simulations to heat and backlight the opacity target. The dominant effect is that the beryllium behind the Fe/Mg mixture is denser and more opaque than the beryllium unshielded by metal during the times of greatest importance for the transmission measurement for all drives. This causes the simulated transmission to be lower than expected, and this is most pronounced for the case using the lowest drive power. While beryllium has a low opacity, its areal density is sufficiently high such that the expected reduction of the measured transmission is significant. This situation leads to an overestimate of iron opacity by 10%-215% for a photon energy range of 975-1775 eV for the 10-TW case. It is shown that if the tamper conditions are known, the transmission through each component of the target can be calculated and the resulting opacity can be corrected.

Community Detection in Signed Networks: the Role of Negative ties in Different Scales

PubMed Central

Esmailian, Pouya; Jalili, Mahdi

2015-01-01

Extracting community structure of complex network systems has many applications from engineering to biology and social sciences. There exist many algorithms to discover community structure of networks. However, it has been significantly under-explored for networks with positive and negative links as compared to unsigned ones. Trying to fill this gap, we measured the quality of partitions by introducing a Map Equation for signed networks. It is based on the assumption that negative relations weaken positive flow from a node towards a community, and thus, external (internal) negative ties increase the probability of staying inside (escaping from) a community. We further extended the Constant Potts Model, providing a map spectrum for signed networks. Accordingly, a partition is selected through balancing between abridgment and expatiation of a signed network. Most importantly, multi-scale spectrum of signed networks revealed how informative are negative ties in different scales, and quantified the topological placement of negative ties between dense positive ones. Moreover, an inconsistency was found in the signed Modularity: as the number of negative ties increases, the density of positive ties is neglected more. These results shed lights on the community structure of signed networks. PMID:26395815

Networked Improvement Communities: The Discipline of Improvement Science Meets the Power of Networks

ERIC Educational Resources Information Center

LeMahieu, Paul G.; Grunow, Alicia; Baker, Laura; Nordstrum, Lee E.; Gomez, Louis M.

2017-01-01

Purpose: The purpose of this paper is to delineate an approach to quality assurance in education called networked improvement communities (NICs) that focused on integrating the methodologies of improvement science with few of the networks. Quality improvement, the science and practice of continuously improving programs, practices, processes,…

Mobilizing Community Museum Networks in Mexico--and Beyond.

ERIC Educational Resources Information Center

Healy, Kevin

2003-01-01

Since the late 1980s, a network of community museums has spread throughout Oaxaca (Mexico), serving as an autonomous force for broad-based cultural development, supporting the maintenance and revitalization of local Indigenous cultures, countering Western cultural hegemony, and involving Indigenous communities in museum development and related…

Community Discovery in Dynamic, Rich-Context Social Networks

ERIC Educational Resources Information Center

Lin, Yu-Ru

2010-01-01

My research interest has been in understanding the human communities formed through interpersonal social activities. Participation in online communities on social network sites such as Twitter has been observed to influence people's behavior in diverse ways including financial decision-making and political choices, suggesting the rich potential…

A fast community detection method in bipartite networks by distance dynamics

NASA Astrophysics Data System (ADS)

Sun, Hong-liang; Ch'ng, Eugene; Yong, Xi; Garibaldi, Jonathan M.; See, Simon; Chen, Duan-bing

2018-04-01

Many real bipartite networks are found to be divided into two-mode communities. In this paper, we formulate a new two-mode community detection algorithm BiAttractor. It is based on distance dynamics model Attractor proposed by Shao et al. with extension from unipartite to bipartite networks. Since Jaccard coefficient of distance dynamics model is incapable to measure distances of different types of vertices in bipartite networks, our main contribution is to extend distance dynamics model from unipartite to bipartite networks using a novel measure Local Jaccard Distance (LJD). Furthermore, distances between different types of vertices are not affected by common neighbors in the original method. This new idea makes clear assumptions and yields interpretable results in linear time complexity O(| E |) in sparse networks, where | E | is the number of edges. Experiments on synthetic networks demonstrate it is capable to overcome resolution limit compared with existing other methods. Further research on real networks shows that this model can accurately detect interpretable community structures in a short time.

Child-resistant and tamper-resistant packaging: A systematic review to inform tobacco packaging regulation.

PubMed

Jo, Catherine L; Ambs, Anita; Dresler, Carolyn M; Backinger, Cathy L

2017-02-01

We aimed to investigate the effects of special packaging (child-resistant, adult-friendly) and tamper-resistant packaging on health and behavioral outcomes in order to identify research gaps and implications for packaging standards for tobacco products. We searched seven databases for keywords related to special and tamper-resistant packaging, consulted experts, and reviewed citations of potentially relevant studies. 733 unique papers were identified. Two coders independently screened each title and abstract for eligibility. They then reviewed the full text of the remaining papers for a second round of eligibility screening. Included studies investigated a causal relationship between type of packaging or packaging regulation and behavioral or health outcomes and had a study population composed of consumers. Studies were excluded on the basis of publication type, if they were not peer-reviewed, and if they had low external validity. Two reviewers independently coded each paper for study and methodological characteristics and limitations. Discrepancies were discussed and resolved. The review included eight studies: four assessing people's ability to access the contents of different packaging types and four evaluating the impact of packaging requirements on health-related outcomes. Child-resistant packaging was generally more difficult to open than non-child-resistant packaging. Child-resistant packaging requirements have been associated with reductions in child mortality. Child-resistant packaging holds the expectation to reduce tobacco product poisonings among children under six. Published by Elsevier Inc.

Community core detection in transportation networks

NASA Astrophysics Data System (ADS)

De Leo, Vincenzo; Santoboni, Giovanni; Cerina, Federica; Mureddu, Mario; Secchi, Luca; Chessa, Alessandro

2013-10-01

This work analyzes methods for the identification and the stability under perturbation of a territorial community structure with specific reference to transportation networks. We considered networks of commuters for a city and an insular region. In both cases, we have studied the distribution of commuters’ trips (i.e., home-to-work trips and vice versa). The identification and stability of the communities’ cores are linked to the land-use distribution within the zone system, and therefore their proper definition may be useful to transport planners.

Identifying the Community Structure of the Food-Trade International Multi-Network

NASA Technical Reports Server (NTRS)

Torreggiani, S.; Mangioni, G.

2018-01-01

Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of international food trade networks are still poorly documented, especially from a multi-network perspective. In particular, nothing is known about the multi-network's community structure. Here we find that the individual crop-specific layers of the multi-network have densely connected trading groups, a consistent characteristic over the period 2001-2011. Further, the multi-network is characterized by low variability over this period but with substantial heterogeneity across layers in each year. In particular, the layers are mostly assortative: more-intensively connected countries tend to import from and export to countries that are themselves more connected. We also fit econometric models to identify social, economic and geographic factors explaining the probability that any two countries are co-present in the same community. Our estimates indicate that the probability of country pairs belonging to the same food trade community depends more on geopolitical and economic factors-such as geographical proximity and trade-agreement co-membership-than on country economic size and/or income. These community-structure findings of the multi-network are especially valuable for efforts to understand past and emerging dynamics in the global food system, especially those that examine potential 'shocks' to global food trade.

Global cluster synchronization in nonlinearly coupled community networks with heterogeneous coupling delays.

PubMed

Tseng, Jui-Pin

2017-02-01

This investigation establishes the global cluster synchronization of complex networks with a community structure based on an iterative approach. The units comprising the network are described by differential equations, and can be non-autonomous and involve time delays. In addition, units in the different communities can be governed by different equations. The coupling configuration of the network is rather general. The coupling terms can be non-diffusive, nonlinear, asymmetric, and with heterogeneous coupling delays. Based on this approach, both delay-dependent and delay-independent criteria for global cluster synchronization are derived. We implement the present approach for a nonlinearly coupled neural network with heterogeneous coupling delays. Two numerical examples are given to show that neural networks can behave in a variety of new collective ways under the synchronization criteria. These examples also demonstrate that neural networks remain synchronized in spite of coupling delays between neurons across different communities; however, they may lose synchrony if the coupling delays between the neurons within the same community are too large, such that the synchronization criteria are violated. Copyright © 2016 Elsevier Ltd. All rights reserved.

Using a two-phase evolutionary framework to select multiple network spreaders based on community structure

NASA Astrophysics Data System (ADS)

Fu, Yu-Hsiang; Huang, Chung-Yuan; Sun, Chuen-Tsai

2016-11-01

Using network community structures to identify multiple influential spreaders is an appropriate method for analyzing the dissemination of information, ideas and infectious diseases. For example, data on spreaders selected from groups of customers who make similar purchases may be used to advertise products and to optimize limited resource allocation. Other examples include community detection approaches aimed at identifying structures and groups in social or complex networks. However, determining the number of communities in a network remains a challenge. In this paper we describe our proposal for a two-phase evolutionary framework (TPEF) for determining community numbers and maximizing community modularity. Lancichinetti-Fortunato-Radicchi benchmark networks were used to test our proposed method and to analyze execution time, community structure quality, convergence, and the network spreading effect. Results indicate that our proposed TPEF generates satisfactory levels of community quality and convergence. They also suggest a need for an index, mechanism or sampling technique to determine whether a community detection approach should be used for selecting multiple network spreaders.

A hierarchical framework for investigating epiphyte assemblages: networks, meta-communities, and scale.

PubMed

Burns, K C; Zotz, G

2010-02-01

Epiphytes are an important component of many forested ecosystems, yet our understanding of epiphyte communities lags far behind that of terrestrial-based plant communities. This discrepancy is exacerbated by the lack of a theoretical context to assess patterns in epiphyte community structure. We attempt to fill this gap by developing an analytical framework to investigate epiphyte assemblages, which we then apply to a data set on epiphyte distributions in a Panamanian rain forest. On a coarse scale, interactions between epiphyte species and host tree species can be viewed as bipartite networks, similar to pollination and seed dispersal networks. On a finer scale, epiphyte communities on individual host trees can be viewed as meta-communities, or suites of local epiphyte communities connected by dispersal. Similar analytical tools are typically employed to investigate species interaction networks and meta-communities, thus providing a unified analytical framework to investigate coarse-scale (network) and fine-scale (meta-community) patterns in epiphyte distributions. Coarse-scale analysis of the Panamanian data set showed that most epiphyte species interacted with fewer host species than expected by chance. Fine-scale analyses showed that epiphyte species richness on individual trees was lower than null model expectations. Therefore, epiphyte distributions were clumped at both scales, perhaps as a result of dispersal limitations. Scale-dependent patterns in epiphyte species composition were observed. Epiphyte-host networks showed evidence of negative co-occurrence patterns, which could arise from adaptations among epiphyte species to avoid competition for host species, while most epiphyte meta-communities were distributed at random. Application of our "meta-network" analytical framework in other locales may help to identify general patterns in the structure of epiphyte assemblages and their variation in space and time.

Terrestrial origin of bacterial communities in complex boreal freshwater networks.

PubMed

Ruiz-González, Clara; Niño-García, Juan Pablo; Del Giorgio, Paul A

2015-08-25

Bacteria inhabiting boreal freshwaters are part of metacommunities where local assemblages are often linked by the flow of water in the landscape, yet the resulting spatial structure and the boundaries of the network metacommunity have never been explored. Here, we reconstruct the spatial structure of the bacterial metacommunity in a complex boreal aquatic network by determining the taxonomic composition of bacterial communities along the entire terrestrial/aquatic continuum, including soil and soilwaters, headwater streams, large rivers and lakes. We show that the network metacommunity has a directional spatial structure driven by a common terrestrial origin of aquatic communities, which are numerically dominated by taxa recruited from soils. Local community assembly is driven by variations along the hydrological continuum in the balance between mass effects and species sorting of terrestrial taxa, and seems further influenced by priority effects related to the spatial sequence of entry of soil bacteria into the network. © 2015 John Wiley & Sons Ltd/CNRS.

Who Networks? The Social Psychology of Virtual Communities

DTIC Science & Technology

2004-06-01

virtual life: the open side - characterized by communities of interest, civil society movements, virtual “states,” and 4 online gaming communities...network of people hailing from Sicily. Sometimes the offline/ online similari- ties mesh even more, as when a gaming society in a small Swedish town...Commercially owned and regulated graphics-based Massively Multi- Player Gaming Communities (EverQuest, The Matrix Online ®, etc.) • UseNET

Semantic Social Network Portal for Collaborative Online Communities

ERIC Educational Resources Information Center

Neumann, Marco; O'Murchu, Ina; Breslin, John; Decker, Stefan; Hogan, Deirdre; MacDonaill, Ciaran

2005-01-01

Purpose: The motivation for this investigation is to apply social networking features to a semantic network portal, which supports the efforts in enterprise training units to up-skill the employee in the company, and facilitates the creation and reuse of knowledge in online communities. Design/methodology/approach: The paper provides an overview…

Fluctuating interaction network and time-varying stability of a natural fish community

NASA Astrophysics Data System (ADS)

Ushio, Masayuki; Hsieh, Chih-Hao; Masuda, Reiji; Deyle, Ethan R.; Ye, Hao; Chang, Chun-Wei; Sugihara, George; Kondoh, Michio

2018-02-01

Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time. Although this theory has experimental support, evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time) and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.

Detection of gene communities in multi-networks reveals cancer drivers

NASA Astrophysics Data System (ADS)

Cantini, Laura; Medico, Enzo; Fortunato, Santo; Caselle, Michele

2015-12-01

We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.

Combining Community Engagement and Scientific Approaches in Next-Generation Monitor Siting: The Case of the Imperial County Community Air Network.

PubMed

Wong, Michelle; Bejarano, Esther; Carvlin, Graeme; Fellows, Katie; King, Galatea; Lugo, Humberto; Jerrett, Michael; Meltzer, Dan; Northcross, Amanda; Olmedo, Luis; Seto, Edmund; Wilkie, Alexa; English, Paul

2018-03-15

Air pollution continues to be a global public health threat, and the expanding availability of small, low-cost air sensors has led to increased interest in both personal and crowd-sourced air monitoring. However, to date, few low-cost air monitoring networks have been developed with the scientific rigor or continuity needed to conduct public health surveillance and inform policy. In Imperial County, California, near the U.S./Mexico border, we used a collaborative, community-engaged process to develop a community air monitoring network that attains the scientific rigor required for research, while also achieving community priorities. By engaging community residents in the project design, monitor siting processes, data dissemination, and other key activities, the resulting air monitoring network data are relevant, trusted, understandable, and used by community residents. Integration of spatial analysis and air monitoring best practices into the network development process ensures that the data are reliable and appropriate for use in research activities. This combined approach results in a community air monitoring network that is better able to inform community residents, support research activities, guide public policy, and improve public health. Here we detail the monitor siting process and outline the advantages and challenges of this approach.

Combining Community Engagement and Scientific Approaches in Next-Generation Monitor Siting: The Case of the Imperial County Community Air Network

PubMed Central

Wong, Michelle; Bejarano, Esther; Carvlin, Graeme; King, Galatea; Lugo, Humberto; Jerrett, Michael; Northcross, Amanda; Olmedo, Luis; Seto, Edmund; Wilkie, Alexa; English, Paul

2018-01-01

Air pollution continues to be a global public health threat, and the expanding availability of small, low-cost air sensors has led to increased interest in both personal and crowd-sourced air monitoring. However, to date, few low-cost air monitoring networks have been developed with the scientific rigor or continuity needed to conduct public health surveillance and inform policy. In Imperial County, California, near the U.S./Mexico border, we used a collaborative, community-engaged process to develop a community air monitoring network that attains the scientific rigor required for research, while also achieving community priorities. By engaging community residents in the project design, monitor siting processes, data dissemination, and other key activities, the resulting air monitoring network data are relevant, trusted, understandable, and used by community residents. Integration of spatial analysis and air monitoring best practices into the network development process ensures that the data are reliable and appropriate for use in research activities. This combined approach results in a community air monitoring network that is better able to inform community residents, support research activities, guide public policy, and improve public health. Here we detail the monitor siting process and outline the advantages and challenges of this approach. PMID:29543726

Accurate detection of hierarchical communities in complex networks based on nonlinear dynamical evolution

NASA Astrophysics Data System (ADS)

Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

2018-04-01

One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community

Rights and Responsibilities of Participants in Networked Communities.

ERIC Educational Resources Information Center

Denning, Dorothy E., Ed.; Lin, Herbert S., Ed.

This report is based on a November 1992 workshop and a February 1993 public forum which discussed some of the social issues raised by the emergence of electronic communities. The workshop examined user, provider, and other perspectives on different types of networked communities, including those on the Internet, commercial information services,…

The Glenview Model: Community Networking via Broadband Cable.

ERIC Educational Resources Information Center

Mundt, John P.

This paper describes the installation of a data network in the community of Glenview, Illinois, which uses broadband cable equipment to connect schools, libraries, and governmental agencies to each other and to the Internet via a high speed Ethernet network. The history of the project is outlined followed by a discussion of the implementation of…

Modelling opinion formation driven communities in social networks

NASA Astrophysics Data System (ADS)

Iñiguez, Gerardo; Barrio, Rafael A.; Kertész, János; Kaski, Kimmo K.

2011-09-01

In a previous paper we proposed a model to study the dynamics of opinion formation in human societies by a co-evolution process involving two distinct time scales of fast transaction and slower network evolution dynamics. In the transaction dynamics we take into account short range interactions as discussions between individuals and long range interactions to describe the attitude to the overall mood of society. The latter is handled by a uniformly distributed parameter α, assigned randomly to each individual, as quenched personal bias. The network evolution dynamics is realised by rewiring the societal network due to state variable changes as a result of transaction dynamics. The main consequence of this complex dynamics is that communities emerge in the social network for a range of values in the ratio between time scales. In this paper we focus our attention on the attitude parameter α and its influence on the conformation of opinion and the size of the resulting communities. We present numerical studies and extract interesting features of the model that can be interpreted in terms of social behaviour.

Community intervention to increase neighborhood social network among Japanese older adults.

PubMed

Harada, Kazuhiro; Masumoto, Kouhei; Katagiri, Keiko; Fukuzawa, Ai; Chogahara, Makoto; Kondo, Narihiko; Okada, Shuichi

2018-03-01

Strengthening neighborhood social networks is important for promoting health among older adults. However, effective intervention strategies aimed at increasing older adults' social networks have not yet been established. The present study examined whether a university-led community intervention that provided communication opportunities could increase older Japanese adults' neighborhood social networks. The present study used a quasi-experimental design. Before the intervention, using postal mail, we carried out a baseline questionnaire survey that was sent to all people living in the Tsurukabuto community aged ≥60 years (n = 1769), of whom 1068 responded. For the community intervention, 18 event-based programs were provided over the course of 1 year at Kobe University. Academic staff at Kobe University organized all the programs. During the program, social interactions among participants were promoted. A follow-up survey was distributed to those who responded to the baseline survey, and 710 individuals answered the question about their participation in the intervention programs (138 respondents were participants, 572 were non-participants). The neighborhood social network was measured in both the baseline and follow-up surveys. Analysis of covariance showed that the changes in neighborhood social network among participants in the program was significantly higher than the changes among non-participants (P = 0.046) after adjusting for the baseline score of social network. The present study found that participants of the intervention expanded their neighborhood social network, but non-participants did not. This finding shows that community interventions using university resources could increase older adults' neighborhood social networks. Geriatr Gerontol Int 2018; 18: 462-469. © 2017 Japan Geriatrics Society.

Improving the Efficiency and Effectiveness of Community Detection via Prior-Induced Equivalent Super-Network.

PubMed

Yang, Liang; Jin, Di; He, Dongxiao; Fu, Huazhu; Cao, Xiaochun; Fogelman-Soulie, Francoise

2017-03-29

Due to the importance of community structure in understanding network and a surge of interest aroused on community detectability, how to improve the community identification performance with pairwise prior information becomes a hot topic. However, most existing semi-supervised community detection algorithms only focus on improving the accuracy but ignore the impacts of priors on speeding detection. Besides, they always require to tune additional parameters and cannot guarantee pairwise constraints. To address these drawbacks, we propose a general, high-speed, effective and parameter-free semi-supervised community detection framework. By constructing the indivisible super-nodes according to the connected subgraph of the must-link constraints and by forming the weighted super-edge based on network topology and cannot-link constraints, our new framework transforms the original network into an equivalent but much smaller Super-Network. Super-Network perfectly ensures the must-link constraints and effectively encodes cannot-link constraints. Furthermore, the time complexity of super-network construction process is linear in the original network size, which makes it efficient. Meanwhile, since the constructed super-network is much smaller than the original one, any existing community detection algorithm is much faster when using our framework. Besides, the overall process will not introduce any additional parameters, making it more practical.

Finding and testing network communities by lumped Markov chains.

PubMed

Piccardi, Carlo

2011-01-01

Identifying communities (or clusters), namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. Yet, there is a lack of formal criteria for defining communities and for testing their significance. We propose a sharp definition that is based on a quality threshold. By means of a lumped Markov chain model of a random walker, a quality measure called "persistence probability" is associated to a cluster, which is then defined as an "α-community" if such a probability is not smaller than α. Consistently, a partition composed of α-communities is an "α-partition." These definitions turn out to be very effective for finding and testing communities. If a set of candidate partitions is available, setting the desired α-level allows one to immediately select the α-partition with the finest decomposition. Simultaneously, the persistence probabilities quantify the quality of each single community. Given its ability in individually assessing each single cluster, this approach can also disclose single well-defined communities even in networks that overall do not possess a definite clusterized structure.

A community detection algorithm using network topologies and rule-based hierarchical arc-merging strategies

PubMed Central

2017-01-01

The authors use four criteria to examine a novel community detection algorithm: (a) effectiveness in terms of producing high values of normalized mutual information (NMI) and modularity, using well-known social networks for testing; (b) examination, meaning the ability to examine mitigating resolution limit problems using NMI values and synthetic networks; (c) correctness, meaning the ability to identify useful community structure results in terms of NMI values and Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks; and (d) scalability, or the ability to produce comparable modularity values with fast execution times when working with large-scale real-world networks. In addition to describing a simple hierarchical arc-merging (HAM) algorithm that uses network topology information, we introduce rule-based arc-merging strategies for identifying community structures. Five well-studied social network datasets and eight sets of LFR benchmark networks were employed to validate the correctness of a ground-truth community, eight large-scale real-world complex networks were used to measure its efficiency, and two synthetic networks were used to determine its susceptibility to two resolution limit problems. Our experimental results indicate that the proposed HAM algorithm exhibited satisfactory performance efficiency, and that HAM-identified and ground-truth communities were comparable in terms of social and LFR benchmark networks, while mitigating resolution limit problems. PMID:29121100

Detecting Network Communities: An Application to Phylogenetic Analysis

PubMed Central

Andrade, Roberto F. S.; Rocha-Neto, Ivan C.; Santos, Leonardo B. L.; de Santana, Charles N.; Diniz, Marcelo V. C.; Lobão, Thierry Petit; Goés-Neto, Aristóteles; Pinho, Suani T. R.; El-Hani, Charbel N.

2011-01-01

This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis. In this case, weights correspond to the similarity indexes among protein sequences, which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties. The analyses discussed here are mainly based on the modular character of protein similarity networks, explored through the Newman-Girvan algorithm, with the help of the neighborhood matrix . The most relevant networks are found when the network topology changes abruptly revealing distinct modules related to the sets of organisms to which the proteins belong. Sound biological information can be retrieved by the computational routines used in the network approach, without using biological assumptions other than those incorporated by BLAST. Usually, all the main bacterial phyla and, in some cases, also some bacterial classes corresponded totally (100%) or to a great extent (>70%) to the modules. We checked for internal consistency in the obtained results, and we scored close to 84% of matches for community pertinence when comparisons between the results were performed. To illustrate how to use the network-based method, we employed data for enzymes involved in the chitin metabolic pathway that are present in more than 100 organisms from an original data set containing 1,695 organisms, downloaded from GenBank on May 19, 2007. A preliminary comparison between the outcomes of the network-based method and the results of methods based on Bayesian, distance, likelihood, and parsimony criteria suggests that the former is as reliable as these commonly used methods. We conclude that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks, which is useful for phylogenetic analysis. PMID:21573202

Assessing Interorganizational Networks as a Dimension of Community Capacity: Illustrations from a Community Intervention to Prevent Lead Poisoning

ERIC Educational Resources Information Center

Singer, Helen Harber; Kegler, Michelle Crozier

2004-01-01

Network analysis is often cited as a method for assessing collaboration among organizations as an indicator of community capacity. The purpose of this study was to (1) document patterns of collaboration in organizational networks related to lead poisoning prevention in a Native American community and (2) examine measurement issues in using…

Identifying the community structure of the food-trade international multi-network

NASA Astrophysics Data System (ADS)

Torreggiani, S.; Mangioni, G.; Puma, M. J.; Fagiolo, G.

2018-05-01

Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of international food trade networks are still poorly documented, especially from a multi-network perspective. In particular, nothing is known about the multi-network’s community structure. Here we find that the individual crop-specific layers of the multi-network have densely connected trading groups, a consistent characteristic over the period 2001–2011. Further, the multi-network is characterized by low variability over this period but with substantial heterogeneity across layers in each year. In particular, the layers are mostly assortative: more-intensively connected countries tend to import from and export to countries that are themselves more connected. We also fit econometric models to identify social, economic and geographic factors explaining the probability that any two countries are co-present in the same community. Our estimates indicate that the probability of country pairs belonging to the same food trade community depends more on geopolitical and economic factors—such as geographical proximity and trade-agreement co-membership—than on country economic size and/or income. These community-structure findings of the multi-network are especially valuable for efforts to understand past and emerging dynamics in the global food system, especially those that examine potential ‘shocks’ to global food trade.

Network community-based model reduction for vortical flows

NASA Astrophysics Data System (ADS)

Gopalakrishnan Meena, Muralikrishnan; Nair, Aditya G.; Taira, Kunihiko

2018-06-01

A network community-based reduced-order model is developed to capture key interactions among coherent structures in high-dimensional unsteady vortical flows. The present approach is data-inspired and founded on network-theoretic techniques to identify important vortical communities that are comprised of vortical elements that share similar dynamical behavior. The overall interaction-based physics of the high-dimensional flow field is distilled into the vortical community centroids, considerably reducing the system dimension. Taking advantage of these vortical interactions, the proposed methodology is applied to formulate reduced-order models for the inter-community dynamics of vortical flows, and predict lift and drag forces on bodies in wake flows. We demonstrate the capabilities of these models by accurately capturing the macroscopic dynamics of a collection of discrete point vortices, and the complex unsteady aerodynamic forces on a circular cylinder and an airfoil with a Gurney flap. The present formulation is found to be robust against simulated experimental noise and turbulence due to its integrating nature of the system reduction.

BEN:LINCS: A Community Model for the Pennsylvania Education Network.

ERIC Educational Resources Information Center

Garrigan, Scott

BEN:LINCS (Bethlehem Education Network: A Local Instructional Network for Culture and Science), a Pennsylvania Testbed Project, attempts to demonstrate a sustainable model that supports network-based educational activities among schools, homes, libraries, museums, and local cultural organizations. The BEN:LINCS project envisioned a community-based…

Improving the recommender algorithms with the detected communities in bipartite networks

NASA Astrophysics Data System (ADS)

Zhang, Peng; Wang, Duo; Xiao, Jinghua

2017-04-01

Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems.

An Enhanced Secure Identity-Based Certificateless Public Key Authentication Scheme for Vehicular Sensor Networks

PubMed Central

Li, Congcong; Zhang, Xi; Wang, Haiping; Li, Dongfeng

2018-01-01

Vehicular sensor networks have been widely applied in intelligent traffic systems in recent years. Because of the specificity of vehicular sensor networks, they require an enhanced, secure and efficient authentication scheme. Existing authentication protocols are vulnerable to some problems, such as a high computational overhead with certificate distribution and revocation, strong reliance on tamper-proof devices, limited scalability when building many secure channels, and an inability to detect hardware tampering attacks. In this paper, an improved authentication scheme using certificateless public key cryptography is proposed to address these problems. A security analysis of our scheme shows that our protocol provides an enhanced secure anonymous authentication, which is resilient against major security threats. Furthermore, the proposed scheme reduces the incidence of node compromise and replication attacks. The scheme also provides a malicious-node detection and warning mechanism, which can quickly identify compromised static nodes and immediately alert the administrative department. With performance evaluations, the scheme can obtain better trade-offs between security and efficiency than the well-known available schemes. PMID:29324719

An Enhanced Secure Identity-Based Certificateless Public Key Authentication Scheme for Vehicular Sensor Networks.

PubMed

Li, Congcong; Zhang, Xi; Wang, Haiping; Li, Dongfeng

2018-01-11

Vehicular sensor networks have been widely applied in intelligent traffic systems in recent years. Because of the specificity of vehicular sensor networks, they require an enhanced, secure and efficient authentication scheme. Existing authentication protocols are vulnerable to some problems, such as a high computational overhead with certificate distribution and revocation, strong reliance on tamper-proof devices, limited scalability when building many secure channels, and an inability to detect hardware tampering attacks. In this paper, an improved authentication scheme using certificateless public key cryptography is proposed to address these problems. A security analysis of our scheme shows that our protocol provides an enhanced secure anonymous authentication, which is resilient against major security threats. Furthermore, the proposed scheme reduces the incidence of node compromise and replication attacks. The scheme also provides a malicious-node detection and warning mechanism, which can quickly identify compromised static nodes and immediately alert the administrative department. With performance evaluations, the scheme can obtain better trade-offs between security and efficiency than the well-known available schemes.

The Quake-Catcher Network: An Innovative Community-Based Seismic Network

NASA Astrophysics Data System (ADS)

Saltzman, J.; Cochran, E. S.; Lawrence, J. F.; Christensen, C. M.

2009-12-01

The Quake-Catcher Network (QCN) is a volunteer computing seismic network that engages citizen scientists, teachers, and museums to participate in the detection of earthquakes. In less than two years, the network has grown to over 1000 participants globally and continues to expand. QCN utilizes Micro-Electro-Mechanical System (MEMS) accelerometers, in laptops and external to desktop computers, to detect moderate to large earthquakes. One goal of the network is to involve K-12 classrooms and museums by providing sensors and software to introduce participants to seismology and community-based scientific data collection. The Quake-Catcher Network provides a unique opportunity to engage participants directly in the scientific process, through hands-on activities that link activities and outcomes to their daily lives. Partnerships with teachers and museum staff are critical to growth of the Quake Catcher Network. Each participating institution receives a MEMS accelerometer to connect, via USB, to a computer that can be used for hands-on activities and to record earthquakes through a distributed computing system. We developed interactive software (QCNLive) that allows participants to view sensor readings in real time. Participants can also record earthquakes and download earthquake data that was collected by their sensor or other QCN sensors. The Quake-Catcher Network combines research and outreach to improve seismic networks and increase awareness and participation in science-based research in K-12 schools.

A Deep Stochastic Model for Detecting Community in Complex Networks

NASA Astrophysics Data System (ADS)

Fu, Jingcheng; Wu, Jianliang

2017-01-01

Discovering community structures is an important step to understanding the structure and dynamics of real-world networks in social science, biology and technology. In this paper, we develop a deep stochastic model based on non-negative matrix factorization to identify communities, in which there are two sets of parameters. One is the community membership matrix, of which the elements in a row correspond to the probabilities of the given node belongs to each of the given number of communities in our model, another is the community-community connection matrix, of which the element in the i-th row and j-th column represents the probability of there being an edge between a randomly chosen node from the i-th community and a randomly chosen node from the j-th community. The parameters can be evaluated by an efficient updating rule, and its convergence can be guaranteed. The community-community connection matrix in our model is more precise than the community-community connection matrix in traditional non-negative matrix factorization methods. Furthermore, the method called symmetric nonnegative matrix factorization, is a special case of our model. Finally, based on the experiments on both synthetic and real-world networks data, it can be demonstrated that our algorithm is highly effective in detecting communities.

An ant colony based algorithm for overlapping community detection in complex networks

NASA Astrophysics Data System (ADS)

Zhou, Xu; Liu, Yanheng; Zhang, Jindong; Liu, Tuming; Zhang, Di

2015-06-01

Community detection is of great importance to understand the structures and functions of networks. Overlap is a significant feature of networks and overlapping community detection has attracted an increasing attention. Many algorithms have been presented to detect overlapping communities. In this paper, we present an ant colony based overlapping community detection algorithm which mainly includes ants' location initialization, ants' movement and post processing phases. An ants' location initialization strategy is designed to identify initial location of ants and initialize label list stored in each node. During the ants' movement phase, the entire ants move according to the transition probability matrix, and a new heuristic information computation approach is redefined to measure similarity between two nodes. Every node keeps a label list through the cooperation made by ants until a termination criterion is reached. A post processing phase is executed on the label list to get final overlapping community structure naturally. We illustrate the capability of our algorithm by making experiments on both synthetic networks and real world networks. The results demonstrate that our algorithm will have better performance in finding overlapping communities and overlapping nodes in synthetic datasets and real world datasets comparing with state-of-the-art algorithms.

Social Networks and Community-Based Natural Resource Management

NASA Astrophysics Data System (ADS)

Lauber, T. Bruce; Decker, Daniel J.; Knuth, Barbara A.

2008-10-01

We conducted case studies of three successful examples of collaborative, community-based natural resource conservation and development. Our purpose was to: (1) identify the functions served by interactions within the social networks of involved stakeholders; (2) describe key structural properties of these social networks; and (3) determine how these structural properties varied when the networks were serving different functions. The case studies relied on semi-structured, in-depth interviews of 8 to 11 key stakeholders at each site who had played a significant role in the collaborative projects. Interview questions focused on the roles played by key stakeholders and the functions of interactions between them. Interactions allowed the exchange of ideas, provided access to funding, and enabled some stakeholders to influence others. The exchange of ideas involved the largest number of stakeholders, the highest percentage of local stakeholders, and the highest density of interactions. Our findings demonstrated the value of tailoring strategies for involving stakeholders to meet different needs during a collaborative, community-based natural resource management project. Widespread involvement of local stakeholders may be most appropriate when ideas for a project are being developed. During efforts to exert influence to secure project approvals or funding, however, involving specific individuals with political connections or influence on possible sources of funds may be critical. Our findings are consistent with past work that has postulated that social networks may require specific characteristics to meet different needs in community-based environmental management.

Think locally, act locally: detection of small, medium-sized, and large communities in large networks.

PubMed

Jeub, Lucas G S; Balachandran, Prakash; Porter, Mason A; Mucha, Peter J; Mahoney, Michael W

2015-01-01

It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic

Think locally, act locally: Detection of small, medium-sized, and large communities in large networks

NASA Astrophysics Data System (ADS)

Jeub, Lucas G. S.; Balachandran, Prakash; Porter, Mason A.; Mucha, Peter J.; Mahoney, Michael W.

2015-01-01

It is common in the study of networks to investigate intermediate-sized (or "meso-scale") features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify "communities," which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that communities are associated with bottlenecks of locally biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for "size-resolved community structure" that can arise in real (and realistic) networks: (1) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (2) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (3) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify "good" communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that, for many large realistic

Attack tolerance of correlated time-varying social networks with well-defined communities

NASA Astrophysics Data System (ADS)

Sur, Souvik; Ganguly, Niloy; Mukherjee, Animesh

2015-02-01

In this paper, we investigate the efficiency and the robustness of information transmission for real-world social networks, modeled as time-varying instances, under targeted attack in shorter time spans. We observe that these quantities are markedly higher than that of the randomized versions of the considered networks. An important factor that drives this efficiency or robustness is the presence of short-time correlations across the network instances which we quantify by a novel metric the-edge emergence factor, denoted as ξ. We find that standard targeted attacks are not effective in collapsing this network structure. Remarkably, if the hourly community structures of the temporal network instances are attacked with the largest size community attacked first, the second largest next and so on, the network soon collapses. This behavior, we show is an outcome of the fact that the edge emergence factor bears a strong positive correlation with the size ordered community structures.

The Healthy Aging Research Network: Modeling Collaboration for Community Impact.

PubMed

Belza, Basia; Altpeter, Mary; Smith, Matthew Lee; Ory, Marcia G

2017-03-01

As the first Centers for Disease Control and Prevention (CDC) Prevention Research Centers Program thematic network, the Healthy Aging Research Network was established to better understand the determinants of healthy aging within older adult populations, identify interventions that promote healthy aging, and assist in translating research into sustainable community-based programs throughout the nation. To achieve these goals requires concerted efforts of a collaborative network of academic, community, and public health organizational partnerships. For the 2001-2014 Prevention Research Center funding cycles, the Healthy Aging Research Network conducted prevention research and promoted the wide use of practices known to foster optimal health. Organized around components necessary for successful collaborations (i.e., governance and infrastructure, shaping focus, community involvement, and evaluation and improvement), this commentary highlights exemplars that demonstrate the Healthy Aging Research Network's unique contributions to the field. The Healthy Aging Research Network's collaboration provided a means to collectively build capacity for practice and policy, reduce fragmentation and duplication in health promotion and aging research efforts, maximize the efficient use of existing resources and generate additional resources, and ultimately, create synergies for advancing the healthy aging agenda. This collaborative model was built upon a backbone organization (coordinating center); setting of common agendas and mutually reinforcing activities; and continuous communications. Given its successes, the Healthy Aging Research Network model could be used to create new and evaluate existing thematic networks to guide the translation of research into policy and practice. Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Child-resistant and tamper-resistant packaging: A systematic review to inform tobacco packaging regulation

PubMed Central

Jo, Catherine L.; Ambs, Anita; Dresler, Carolyn M.; Backinger, Cathy L.

2017-01-01

Objective We aimed to investigate the effects of special packaging (child-resistant, adult-friendly) and tamper-resistant packaging on health and behavioral outcomes in order to identify research gaps and implications for packaging standards for tobacco products. Methods We searched seven databases for keywords related to special and tamper-resistant packaging, consulted experts, and reviewed citations of potentially relevant studies. 733 unique papers were identified. Two coders independently screened each title and abstract for eligibility. They then reviewed the full text of the remaining papers for a second round of eligibility screening. Included studies investigated a causal relationship between type of packaging or packaging regulation and behavioral or health outcomes and had a study population composed of consumers. Studies were excluded on the basis of publication type, if they were not peer-reviewed, and if they had low external validity. Two reviewers independently coded each paper for study and methodological characteristics and limitations. Discrepancies were discussed and resolved. Results The review included eight studies: four assessing people’s ability to access the contents of different packaging types and four evaluating the impact of packaging requirements on health-related outcomes. Child-resistant packaging was generally more difficult to open than non-child-resistant packaging. Child-resistant packaging requirements have been associated with reductions in child mortality. Conclusions Child-resistant packaging holds the expectation to reduce tobacco product poisonings among children under six. PMID:27939602

Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics

PubMed Central

Kovács, István A.; Palotai, Robin; Szalay, Máté S.; Csermely, Peter

2010-01-01

Background Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction. PMID:20824084

78 FR 41088 - Solicitation for a Cooperative Agreement-Support Services for Community Services Division Networks

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-09

...--Support Services for Community Services Division Networks AGENCY: National Institute of Corrections, U.S... cooperative agreement will provide support services to NIC Community Services Division sponsored networks. The networks are designed for NIC to assist in meeting the needs of the field of community corrections by...

Simulating Social Networks of Online Communities: Simulation as a Method for Sociability Design

NASA Astrophysics Data System (ADS)

Ang, Chee Siang; Zaphiris, Panayiotis

We propose the use of social simulations to study and support the design of online communities. In this paper, we developed an Agent-Based Model (ABM) to simulate and study the formation of social networks in a Massively Multiplayer Online Role Playing Game (MMORPG) guild community. We first analyzed the activities and the social network (who-interacts-with-whom) of an existing guild community to identify its interaction patterns and characteristics. Then, based on the empirical results, we derived and formalized the interaction rules, which were implemented in our simulation. Using the simulation, we reproduced the observed social network of the guild community as a means of validation. The simulation was then used to examine how various parameters of the community (e.g. the level of activity, the number of neighbors of each agent, etc) could potentially influence the characteristic of the social networks.

Investigating Student Communities with Network Analysis of Interactions in a Physics Learning Center

NASA Astrophysics Data System (ADS)

Brewe, Eric; Kramer, Laird; O'Brien, George

2009-11-01

We describe our initial efforts at implementing social network analysis to visualize and quantify student interactions in Florida International University's Physics Learning Center. Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at FIU. Our implementation of a research and learning community, embedded within a course reform effort, has led to increased recruitment and retention of physics majors. Finn and Rock [1997] link the academic and social integration of students to increased rates of retention. To identify these interactions, we have initiated an investigation that utilizes social network analysis to identify primary community participants. Community interactions are then characterized through the network's density and connectivity, shedding light on learning communities and participation. Preliminary results, further research questions, and future directions utilizing social network analysis are presented.

Formation of Community-Based Hypertension Practice Networks: Success, Obstacles, and Lessons Learned

PubMed Central

Dart, Richard A.; Egan, Brent M.

2014-01-01

Community-based practice networks for research and improving the quality of care are growing in size and number but have variable success rates. In this paper we review recent efforts to initiate a community-based hypertension network modeled after the successful Outpatient Quality Improvement Network (O’QUIN) project, located at the Medical University of South Carolina. We highlight key lessons learned and new directions to be explored. PMID:24666425

Surveying traffic congestion based on the concept of community structure of complex networks

NASA Astrophysics Data System (ADS)

Ma, Lili; Zhang, Zhanli; Li, Meng

2016-07-01

In this paper, taking the traffic of Beijing city as an instance, we study city traffic states, especially traffic congestion, based on the concept of network community structure. Concretely, using the floating car data (FCD) information of vehicles gained from the intelligent transport system (ITS) of the city, we construct a new traffic network model which is with floating cars as network nodes and time-varying. It shows that this traffic network has Gaussian degree distributions at different time points. Furthermore, compared with free traffic situations, our simulations show that the traffic network generally has more obvious community structures with larger values of network fitness for congested traffic situations, and through the GPSspg web page, we show that all of our results are consistent with the reality. Then, it indicates that network community structure should be an available way for investigating city traffic congestion problems.

A Community "Hub" Network Intervention for HIV Stigma Reduction: A Case Study.

PubMed

Prinsloo, Catharina D; Greeff, Minrie

2016-01-01

We describe the implementation of a community "hub" network intervention to reduce HIV stigma in the Tlokwe Municipality, North West Province, South Africa. A holistic case study design was used, focusing on community members with no differentiation by HIV status. Participants were recruited through accessibility sampling. Data analyses used open coding and document analysis. Findings showed that the HIV stigma-reduction community hub network intervention successfully activated mobilizers to initiate change; lessened the stigma experience for people living with HIV; and addressed HIV stigma in a whole community using a combination of strategies including individual and interpersonal levels, social networks, and the public. Further research is recommended to replicate and enhance the intervention. In particular, the hub network system should be extended, the intervention period should be longer, there should be a stronger support system for mobilizers, and the multiple strategy approach should be continued on individual and social levels. Copyright © 2016 Association of Nurses in AIDS Care. Published by Elsevier Inc. All rights reserved.

Clustering algorithm for determining community structure in large networks

NASA Astrophysics Data System (ADS)

Pujol, Josep M.; Béjar, Javier; Delgado, Jordi

2006-07-01

We propose an algorithm to find the community structure in complex networks based on the combination of spectral analysis and modularity optimization. The clustering produced by our algorithm is as accurate as the best algorithms on the literature of modularity optimization; however, the main asset of the algorithm is its efficiency. The best match for our algorithm is Newman’s fast algorithm, which is the reference algorithm for clustering in large networks due to its efficiency. When both algorithms are compared, our algorithm outperforms the fast algorithm both in efficiency and accuracy of the clustering, in terms of modularity. Thus, the results suggest that the proposed algorithm is a good choice to analyze the community structure of medium and large networks in the range of tens and hundreds of thousand vertices.

Variability of community interaction networks in marine reserves and adjacent exploited areas

USGS Publications Warehouse

Montano-Moctezuma, G.; Li, H.W.; Rossignol, P.A.

2008-01-01

Regional and small-scale local oceanographic conditions can lead to high variability in community structure even among similar habitats. Communities with identical species composition can depict distinct networks due to different levels of disturbance as well as physical and biological processes. In this study we reconstruct community networks in four different areas off the Oregon Coast by matching simulated communities with observed dynamics. We compared reserves with harvested areas. Simulations suggested that different community networks, but with the same species composition, can represent each study site. Differences were found in predator-prey interactions as well as non-predatory interactions between community members. In addition, each site can be represented as a set of models, creating alternative stages among sites. The set of alternative models that characterize each study area depicts a sequence of functional responses where each specific model or interaction structure creates different species composition patterns. Different management practices, either in the past or of the present, may lead to alternative communities. Our findings suggest that management strategies should be analyzed at a community level that considers the possible consequences of shifting from one community scenario to another. This analysis provides a novel conceptual framework to assess the consequences of different management options for ecological communities. ?? 2008 Elsevier B.V. All rights reserved.

Community Structure in Social Networks: Applications for Epidemiological Modelling

PubMed Central

Kitchovitch, Stephan; Liò, Pietro

2011-01-01

During an infectious disease outbreak people will often change their behaviour to reduce their risk of infection. Furthermore, in a given population, the level of perceived risk of infection will vary greatly amongst individuals. The difference in perception could be due to a variety of factors including varying levels of information regarding the pathogen, quality of local healthcare, availability of preventative measures, etc. In this work we argue that we can split a social network, representing a population, into interacting communities with varying levels of awareness of the disease. We construct a theoretical population and study which such communities suffer most of the burden of the disease and how their awareness affects the spread of infection. We aim to gain a better understanding of the effects that community-structured networks and variations in awareness, or risk perception, have on the disease dynamics and to promote more community-resolved modelling in epidemiology. PMID:21789238

George Washington Community High School: analysis of a partnership network.

PubMed

Bringle, Robert G; Officer, Starla D H; Grim, Jim; Hatcher, Julie A

2009-01-01

After five years with no public schools in their community, residents and neighborhood organizations of the Near Westside of Indianapolis advocated for the opening of George Washington Community High School (GWCHS). As a neighborhood in close proximity to the campus of Indiana University-Purdue University Indianapolis, the Near Westside and campus worked together to address this issue and improve the educational success of youth. In fall 2000, GWCHS opened as a community school and now thrives as a national model, due in part to its network of community relationships. This account analyzes the development of the school by focusing on the relationships among the university, the high school, community organizations, and the residents of the Near Westside and highlights the unique partnership between the campus and school by defining the relational qualities and describing the network created to make sustainable changes with the high school.

The Rise of China in the International Trade Network: A Community Core Detection Approach

PubMed Central

Zhu, Zhen; Cerina, Federica; Chessa, Alessandro; Caldarelli, Guido; Riccaboni, Massimo

2014-01-01

Theory of complex networks proved successful in the description of a variety of complex systems ranging from biology to computer science and to economics and finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995–2011. We find rich dynamics over time both inter- and intra-communities. In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. We provide a multilevel description of the evolution of the network where the global dynamics (i.e., communities disappear or reemerge) and the regional dynamics (i.e., community core changes between community members) are related. Moreover, simulation results show that the global dynamics can be generated by a simple dynamic-edge-weight mechanism. PMID:25136895

The rise of China in the International Trade Network: a community core detection approach.

PubMed

Zhu, Zhen; Cerina, Federica; Chessa, Alessandro; Caldarelli, Guido; Riccaboni, Massimo

2014-01-01

Theory of complex networks proved successful in the description of a variety of complex systems ranging from biology to computer science and to economics and finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995-2011. We find rich dynamics over time both inter- and intra-communities. In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. We provide a multilevel description of the evolution of the network where the global dynamics (i.e., communities disappear or reemerge) and the regional dynamics (i.e., community core changes between community members) are related. Moreover, simulation results show that the global dynamics can be generated by a simple dynamic-edge-weight mechanism.

Associate degree nursing in a community-based health center network: lessons in collaboration.

PubMed

Connolly, Charlene; Wilson, Diane; Missett, Regina; Dooley, Wanda C; Avent, Pamela A; Wright, Ronda

2004-02-01

This exemplar highlights the ability of community experiences to enhance nursing students' understanding of the principles of community-based care: advocating self-care; focusing on prevention, family, culture, and community; providing continuity of care; and collaborating. An innovative teaching-practice model (i.e., a nurse-managed "network" of clinics), incorporating service-learning, was created. The Network's purposes are to provide practice sites in community-based primary care settings for student clinical rotations, increasing the awareness of the civic and social responsibility to provide quality health care for disadvantaged populations; and to reduce health disparities by increasing access to free primary health care, including health promotion and disease prevention, for disadvantaged individuals. Network clients receive free health care, referrals, and guidance to effectively obtain additional health care resources for themselves and their families. The Network is a national pioneer in modeling the delivery of primary care services through a faculty-student practice plan, with leadership emanating from a community college.

Formation of community-based hypertension practice networks: success, obstacles, and lessons learned.

PubMed

Dart, Richard A; Egan, Brent M

2014-06-01

Community-based practice networks for research and improving the quality of care are growing in size and number but have variable success rates. In this paper, the authors review recent efforts to initiate a community-based hypertension network modeled after the successful Outpatient Quality Improvement Network (O'QUIN) project, located at the Medical University of South Carolina. Key lessons learned and new directions to be explored are highlighted. ©2014 Wiley Periodicals, Inc.

Towards a Community Environmental Observation Network

NASA Astrophysics Data System (ADS)

Mertl, Stefan; Lettenbichler, Anton

2014-05-01

The Community Environmental Observation Network (CEON) is dedicated to the development of a free sensor network to collect and distribute environmental data (e.g. ground shaking, climate parameters). The data collection will be done with contributions from citizens, research institutions and public authorities like communities or schools. This will lead to a large freely available data base which can be used for public information, research, the arts,..... To start a free sensor network, the most important step is to provide easy access to free data collection and -distribution tools. The initial aims of the project CEON are dedicated to the development of these tools. A high quality data logger based on open hardware and free software is developed and a software suite of already existing free software for near-real time data communication and data distribution over the Internet will be assembled. Foremost, the development focuses on the collection of data related to the deformation of the earth (such as ground shaking, surface displacement of mass movements and glaciers) and the collection of climate data. The extent to other measurements will be considered in the design. The data logger is built using open hardware prototyping platforms like BeagleBone Black and Arduino. Main features of the data logger are: a 24Bit analog-to-digital converter; a GPS module for time reference and positioning; wireless mesh networking using Optimized Link State Routing; near real-time data transmission and communication; and near real-time differential GNSS positioning using the RTKLIB software. The project CEON is supported by the Internet Foundation Austria (IPA) within the NetIdee 2013 call.

Making big communities small: using network science to understand the ecological and behavioral requirements for community social capital.

PubMed

Neal, Zachary

2015-06-01

The concept of social capital is becoming increasingly common in community psychology and elsewhere. However, the multiple conceptual and operational definitions of social capital challenge its utility as a theoretical tool. The goals of this paper are to clarify two forms of social capital (bridging and bonding), explicitly link them to the structural characteristics of small world networks, and explore the behavioral and ecological prerequisites of its formation. First, I use the tools of network science and specifically the concept of small-world networks to clarify what patterns of social relationships are likely to facilitate social capital formation. Second, I use an agent-based model to explore how different ecological characteristics (diversity and segregation) and behavioral tendencies (homophily and proximity) impact communities' potential for developing social capital. The results suggest diverse communities have the greatest potential to develop community social capital, and that segregation moderates the effects that the behavioral tendencies of homophily and proximity have on community social capital. The discussion highlights how these findings provide community-based researchers with both a deeper understanding of the contextual constraints with which they must contend, and a useful tool for targeting their efforts in communities with the greatest need or greatest potential.

Local communities obstruct global consensus: Naming game on multi-local-world networks

NASA Astrophysics Data System (ADS)

Lou, Yang; Chen, Guanrong; Fan, Zhengping; Xiang, Luna

2018-02-01

Community structure is essential for social communications, where individuals belonging to the same community are much more actively interacting and communicating with each other than those in different communities within the human society. Naming game, on the other hand, is a social communication model that simulates the process of learning a name of an object within a community of humans, where the individuals can generally reach global consensus asymptotically through iterative pair-wise conversations. The underlying network indicates the relationships among the individuals. In this paper, three typical topologies, namely random-graph, small-world and scale-free networks, are employed, which are embedded with the multi-local-world community structure, to study the naming game. Simulations show that (1) the convergence process to global consensus is getting slower as the community structure becomes more prominent, and eventually might fail; (2) if the inter-community connections are sufficiently dense, neither the number nor the size of the communities affects the convergence process; and (3) for different topologies with the same (or similar) average node-degree, local clustering of individuals obstruct or prohibit global consensus to take place. The results reveal the role of local communities in a global naming game in social network studies.

Relating Diarrheal Disease to Social Networks and the Geographic Configuration of Communities in Rural Ecuador

PubMed Central

Bates, Sarah J.; Trostle, James; Cevallos, William T.; Hubbard, Alan; Eisenberg, Joseph N. S.

2008-01-01

Social networks and geographic structures of communities are important predictors of infectious disease transmission. To examine their joint effects on diarrheal disease and how these effects might develop, the authors analyzed social network and geographic data from northern coastal Ecuador and examined associations with diarrhea prevalence. Between July 2003 and May 2005, 113 cases of diarrhea were identified in nine communities. Concurrently, sociometric surveys were conducted, and households were mapped with geographic information systems. Spatial distribution metrics of households within communities and of communities with respect to roads were developed that predict social network degree in casual contact (“contact”) and food-sharing (“food”) networks. The mean degree is 25-40% lower in communities with versus without road access and 66-94% lower in communities with lowest versus highest housing density. Associations with diarrheal disease were found for housing density (comparing dense with dispersed communities: risk ratio = 3.3, 95% confidence interval (CI): 1.1, 10.0) and social connectedness (comparing lowest with highest degree communities: risk ratio = 3.4, 95% CI: 1.1, 10.1 in the contact network and risk ratio = 4.9, 95% CI: 1.1, 21.9 in the food network). Some of these differences may be related to more new residents, lower housing density, and less social connectedness in road communities. PMID:17690221

Fiber Optic Tamper Indicating Enclosure (TIE); A Case Study in Authentication

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anheier, Norman C.; Benz, Jacob M.; Tanner, Jennifer E.

2015-07-15

A robust fiber optic-based tamper-indicating enclosure (TIE) has been developed by PNNL through funding by the National Nuclear Security Administration Office of Nuclear Verification over the past few years. The objective of this work is to allow monitors to have confidence in both the authenticity and integrity of the TIE and the monitoring equipment inside, throughout the time it may be located at a host facility. Incorporating authentication features into the design were the focus of fiscal year 2014 development efforts. Throughout the development process, modifications have been made to the physical TIE design based on lessons learned via exercisesmore » and expert elicitation. The end result is a robust and passive TIE which can be utilized to protect monitoring party equipment left in a host facility.« less

Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks

NASA Astrophysics Data System (ADS)

Cui, Yaozu; Wang, Xingyuan; Eustace, Justine

2014-12-01

Community structure is a common phenomenon in complex networks, and it has been shown that some communities in complex networks often overlap each other. So in this paper we propose a new algorithm to detect overlapping community structure in complex networks. To identify the overlapping community structure, our algorithm firstly extracts fully connected sub-graphs which are maximal sub-graphs from original networks. Then two maximal sub-graphs having the key pair-vertices can be merged into a new larger sub-graph using some belonging degree functions. Furthermore we extend the modularity function to evaluate the proposed algorithm. In addition, overlapping nodes between communities are founded successfully. Finally we report the comparison between the modularity and the computational complexity of the proposed algorithm with some other existing algorithms. The experimental results show that the proposed algorithm gives satisfactory results.

Network exposure and homicide victimization in an African American community.

PubMed

Papachristos, Andrew V; Wildeman, Christopher

2014-01-01

We estimated the association of an individual's exposure to homicide in a social network and the risk of individual homicide victimization across a high-crime African American community. Combining 5 years of homicide and police records, we analyzed a network of 3718 high-risk individuals that was created by instances of co-offending. We used logistic regression to model the odds of being a gunshot homicide victim by individual characteristics, network position, and indirect exposure to homicide. Forty-one percent of all gun homicides occurred within a network component containing less than 4% of the neighborhood's population. Network-level indicators reduced the association between individual risk factors and homicide victimization and improved the overall prediction of individual victimization. Network exposure to homicide was strongly associated with victimization: the closer one is to a homicide victim, the greater the risk of victimization. Regression models show that exposure diminished with social distance: each social tie removed from a homicide victim decreased one's odds of being a homicide victim by 57%. Risk of homicide in urban areas is even more highly concentrated than previously thought. We found that most of the risk of gun violence was concentrated in networks of identifiable individuals. Understanding these networks may improve prediction of individual homicide victimization within disadvantaged communities.

Practice-based Research Networks (PBRNs) Bridging the Gaps between Communities, Funders, and Policymakers.

PubMed

Gaglioti, Anne H; Werner, James J; Rust, George; Fagnan, Lyle J; Neale, Anne Victoria

2016-01-01

In this commentary, we propose that practice-based research networks (PBRNs) engage with funders and policymakers by applying the same engagement strategies they have successfully used to build relationships with community stakeholders. A community engagement approach to achieve new funding streams for PBRNs should include a strategy to engage key stakeholders from the communities of funders, thought leaders, and policymakers using collaborative principles and methods. PBRNs that implement this strategy would build a robust network of engaged partners at the community level, across networks, and would reach state and federal policymakers, academic family medicine departments, funding bodies, and national thought leaders in the redesign of health care delivery. © Copyright 2016 by the American Board of Family Medicine.

Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks

NASA Astrophysics Data System (ADS)

Ghasemian, Amir; Zhang, Pan; Clauset, Aaron; Moore, Cristopher; Peel, Leto

2016-07-01

The detection of communities within a dynamic network is a common means for obtaining a coarse-grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information.

Teachers Learning in Networked Communities. Phase I Evaluation Report

ERIC Educational Resources Information Center

Carroll, Tom; Fulton, Kathleen; Yoon, Irene

2005-01-01

In 2003 the National Commission on Teaching and America's Future convened a design team to launch the Teachers Learning in Networked Communities (TLINC) project. The initial one-year phase, funded by AT&T, involved a TLINC design team partnered with four communities, Pueblo, Colorado; Seattle, Washington; Portland, Maine; and Socorro, Texas. The…

Combined node and link partitions method for finding overlapping communities in complex networks

PubMed Central

Jin, Di; Gabrys, Bogdan; Dang, Jianwu

2015-01-01

Community detection in complex networks is a fundamental data analysis task in various domains, and how to effectively find overlapping communities in real applications is still a challenge. In this work, we propose a new unified model and method for finding the best overlapping communities on the basis of the associated node and link partitions derived from the same framework. Specifically, we first describe a unified model that accommodates node and link communities (partitions) together, and then present a nonnegative matrix factorization method to learn the parameters of the model. Thereafter, we infer the overlapping communities based on the derived node and link communities, i.e., determine each overlapped community between the corresponding node and link community with a greedy optimization of a local community function conductance. Finally, we introduce a model selection method based on consensus clustering to determine the number of communities. We have evaluated our method on both synthetic and real-world networks with ground-truths, and compared it with seven state-of-the-art methods. The experimental results demonstrate the superior performance of our method over the competing ones in detecting overlapping communities for all analysed data sets. Improved performance is particularly pronounced in cases of more complicated networked community structures. PMID:25715829

Value Co-creation and Co-innovation: Linking Networked Organisations and Customer Communities

NASA Astrophysics Data System (ADS)

Romero, David; Molina, Arturo

Strategic networks such as Collaborative Networked Organisations (CNOs) and Virtual Customer Communities (VCCs) show a high potential as drivers of value co-creation and collaborative innovation in today’s Networking Era. Both look at the network structures as a source of jointly value creation and open innovation through access to new skills, knowledge, markets and technologies by sharing risk and integrating complementary competencies. This collaborative endeavour has proven to be able to enhance the adaptability and flexibility of CNOs and VCCs value creating systems in order to react in response to external drivers such as collaborative (business) opportunities. This paper presents a reference framework for creating interface networks, also known as ‘experience-centric networks’, as enablers for linking networked organisations and customer communities in order to support the establishment of user-driven and collaborative innovation networks.

Think Locally, Act Locally: The Detection of Small, Medium-Sized, and Large Communities in Large Networks

PubMed Central

Jeub, Lucas G. S.; Balachandran, Prakash; Porter, Mason A.; Mucha, Peter J.; Mahoney, Michael W.

2016-01-01

It is common in the study of networks to investigate intermediate-sized (or “meso-scale”) features to try to gain an understanding of network structure and function. For example, numerous algorithms have been developed to try to identify “communities,” which are typically construed as sets of nodes with denser connections internally than with the remainder of a network. In this paper, we adopt a complementary perspective that “communities” are associated with bottlenecks of locally-biased dynamical processes that begin at seed sets of nodes, and we employ several different community-identification procedures (using diffusion-based and geodesic-based dynamics) to investigate community quality as a function of community size. Using several empirical and synthetic networks, we identify several distinct scenarios for “size-resolved community structure” that can arise in real (and realistic) networks: (i) the best small groups of nodes can be better than the best large groups (for a given formulation of the idea of a good community); (ii) the best small groups can have a quality that is comparable to the best medium-sized and large groups; and (iii) the best small groups of nodes can be worse than the best large groups. As we discuss in detail, which of these three cases holds for a given network can make an enormous difference when investigating and making claims about network community structure, and it is important to take this into account to obtain reliable downstream conclusions. Depending on which scenario holds, one may or may not be able to successfully identify “good” communities in a given network (and good communities might not even exist for a given community quality measure), the manner in which different small communities fit together to form meso-scale network structures can be very different, and processes such as viral propagation and information diffusion can exhibit very different dynamics. In addition, our results suggest that

[Study on dilemma and strategy of community support network by community organization for prevention of isolated death in an urban area].

PubMed

Masuda, Yuzuri; Tadaka, Etsuko; Dai, Yuka; Itoi, Waka; Taguchi, Rie; Kawahara, Chie

2011-12-01

Isolated death of elderly is recognized as a severe social problem in public health and it is an urgent requirement that a supportive community network be organized so that its occurrence is minimized. The purpose of this research was to analyze actual issues of a supportive community network for elderly within the community and to obtain clues for useful actions to prevent isolated death of elderly individuals in the future. The subjects were 14 representatives of a supportive community network for elderly in A City, B Ward and C District (as a junior high school segment). The research was conducted with a qualitative inductively approach using the Focus Group Interview (FGI). Interviews were focused on difficulties and perspectives within their daily support activities in the community, and were held three times during October 2009 to March 2010. The FGI records were then analyzed with meaningful minimal words and sentences, categorized codes, and then those codes were classified into subcategories or categories. Three categories, Individual, Neighborhood and Community network for elderly resulted from the analysis. Regarding difficulties, "Refusing supports or indifference", "Isolation or Tojikomori in the youth generation", "Lack of family support", "Relationships among their residents weakening gradually", "Unfamiliar newcomers and residents", "Residence feels burden on association with neighborhood", "Limitation of support activities under personal security", "Lack of resources for persons and places of gathering" were identified. On the other hand, perspectives in the community network for elderly were "Building relationships personally", "Invitation to community meetings as companions", "Development of safety confirmation", "Helping each other in the neighborhood", "Stimulate enforcement of bonding in daily life", "Making arrangements for regional administration and residents for supportive activites", "Fostering the trust and connection of residence". To

Diabetes education project: community networking in rural Utah.

PubMed

DeBry, S M; Smith, A; Wittenberg, M; Mortensen, V

1996-01-01

People in rural areas often lack the financial resources, workforce, and professional network needed to sustain a diabetes education pro gram in their own community. HealthInsight, a nonprofit organization that works to improve the quality of health care in its community, developed a 2-day seminar in an effort to facilitate the networking of rural health professionals who educate patients with diabetes and to help those educators better learn how to use existing resources. Participants included nurses, dietitians, diabetes educators, quality managers, and education directors from hospitals and home health agencies in both rural and metropolitan areas. Speakers presented information on a variety of topics related to program development, and a resource manual containing numerous materials was given to each participant. At the end of the seminar, the group turned in goals for their own programs. Too often, providers of health care compete rather than collaborate with one another. There is a great need for such networking opportunities among health care professionals working on common goals--especially in rural areas.

Clustering Financial Time Series by Network Community Analysis

NASA Astrophysics Data System (ADS)

Piccardi, Carlo; Calatroni, Lisa; Bertoni, Fabio

In this paper, we describe a method for clustering financial time series which is based on community analysis, a recently developed approach for partitioning the nodes of a network (graph). A network with N nodes is associated to the set of N time series. The weight of the link (i, j), which quantifies the similarity between the two corresponding time series, is defined according to a metric based on symbolic time series analysis, which has recently proved effective in the context of financial time series. Then, searching for network communities allows one to identify groups of nodes (and then time series) with strong similarity. A quantitative assessment of the significance of the obtained partition is also provided. The method is applied to two distinct case-studies concerning the US and Italy Stock Exchange, respectively. In the US case, the stability of the partitions over time is also thoroughly investigated. The results favorably compare with those obtained with the standard tools typically used for clustering financial time series, such as the minimal spanning tree and the hierarchical tree.

Mapping the ecological networks of microbial communities.

PubMed

Xiao, Yandong; Angulo, Marco Tulio; Friedman, Jonathan; Waldor, Matthew K; Weiss, Scott T; Liu, Yang-Yu

2017-12-11

Mapping the ecological networks of microbial communities is a necessary step toward understanding their assembly rules and predicting their temporal behavior. However, existing methods require assuming a particular population dynamics model, which is not known a priori. Moreover, those methods require fitting longitudinal abundance data, which are often not informative enough for reliable inference. To overcome these limitations, here we develop a new method based on steady-state abundance data. Our method can infer the network topology and inter-taxa interaction types without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka-Volterra model, our method can infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental data sets. Our method represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.

From calls to communities: a model for time-varying social networks

NASA Astrophysics Data System (ADS)

Laurent, Guillaume; Saramäki, Jari; Karsai, Márton

2015-11-01

Social interactions vary in time and appear to be driven by intrinsic mechanisms that shape the emergent structure of social networks. Large-scale empirical observations of social interaction structure have become possible only recently, and modelling their dynamics is an actual challenge. Here we propose a temporal network model which builds on the framework of activity-driven time-varying networks with memory. The model integrates key mechanisms that drive the formation of social ties - social reinforcement, focal closure and cyclic closure, which have been shown to give rise to community structure and small-world connectedness in social networks. We compare the proposed model with a real-world time-varying network of mobile phone communication, and show that they share several characteristics from heterogeneous degrees and weights to rich community structure. Further, the strong and weak ties that emerge from the model follow similar weight-topology correlations as real-world social networks, including the role of weak ties.

Community detection in complex networks using proximate support vector clustering

NASA Astrophysics Data System (ADS)

Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

2018-03-01

Community structure, one of the most attention attracting properties in complex networks, has been a cornerstone in advances of various scientific branches. A number of tools have been involved in recent studies concentrating on the community detection algorithms. In this paper, we propose a support vector clustering method based on a proximity graph, owing to which the introduced algorithm surpasses the traditional support vector approach both in accuracy and complexity. Results of extensive experiments undertaken on computer generated networks and real world data sets illustrate competent performances in comparison with the other counterparts.

The role of stabilizing and communicating symptoms given overlapping communities in psychopathology networks.

PubMed

Blanken, Tessa F; Deserno, Marie K; Dalege, Jonas; Borsboom, Denny; Blanken, Peter; Kerkhof, Gerard A; Cramer, Angélique O J

2018-04-11

Network theory, as a theoretical and methodological framework, is energizing many research fields, among which clinical psychology and psychiatry. Fundamental to the network theory of psychopathology is the role of specific symptoms and their interactions. Current statistical tools, however, fail to fully capture this constitutional property. We propose community detection tools as a means to evaluate the complex network structure of psychopathology, free from its original boundaries of distinct disorders. Unique to this approach is that symptoms can belong to multiple communities. Using a large community sample and spanning a broad range of symptoms (Symptom Checklist-90-Revised), we identified 18 communities of interconnected symptoms. The differential role of symptoms within and between communities offers a framework to study the clinical concepts of comorbidity, heterogeneity and hallmark symptoms. Symptoms with many and strong connections within a community, defined as stabilizing symptoms, could be thought of as the core of a community, whereas symptoms that belong to multiple communities, defined as communicating symptoms, facilitate the communication between problem areas. We propose that defining symptoms on their stabilizing and/or communicating role within and across communities accelerates our understanding of these clinical phenomena, central to research and treatment of psychopathology.

Utilization of an interorganizational network analysis to evaluate the development of community capacity among a community-academic partnership.

PubMed

Clark, Heather R; Ramirez, Albert; Drake, Kelly N; Beaudoin, Christopher E; Garney, Whitney R; Wendel, Monica L; Outley, Corliss; Burdine, James N; Player, Harold D

2014-01-01

Following a community health assessment the Brazos Valley Health Partnership (BVHP) organized to address fragmentation of services and local health needs. This regional partnership employs the fundamental principles of community-based participatory research, fostering an equitable partnership with the aim of building community capacity to address local health issues. This article describes changes in relationships as a result of capacity building efforts in a community-academic partnership. Growth in network structure among organizations is hypothesized to be indicative of less fragmentation of services for residents and increased capacity of the BVHP to collectively address local health issues. Each of the participant organizations responded to a series of questions regarding its relationships with other organizations. Each organization was asked about information sharing, joint planning, resource sharing, and formal agreements with other organizations. The network survey has been administered 3 times between 2004 and 2009. Network density increased for sharing information and jointly planning events. Growth in the complexity of relationships was reported for sharing tangible resources and formal agreements. The average number of ties between organizations as well as the strength of relationships increased. This study provides evidence that the community capacity building efforts within these communities have contributed to beneficial changes in interorganizational relationships. Results from this analysis are useful for understanding how a community partnership's efforts to address access to care can strengthen a community's capacity for future action. Increased collaboration also leads to new assets, resources, and the transfer of knowledge and skills.

Discrete particle swarm optimization for identifying community structures in signed social networks.

PubMed

Cai, Qing; Gong, Maoguo; Shen, Bo; Ma, Lijia; Jiao, Licheng

2014-10-01

Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles' status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles' updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Copyright © 2014 Elsevier Ltd. All rights reserved.

Uncovering the community structure in signed social networks based on greedy optimization

NASA Astrophysics Data System (ADS)

Chen, Yan; Yan, Jiaqi; Yang, Yu; Chen, Junhua

2017-05-01

The formality of signed relationships has been recently adopted in a lot of complicated systems. The relations among these entities are complicated and multifarious. We cannot indicate these relationships only by positive links, and signed networks have been becoming more and more universal in the study of social networks when community is being significant. In this paper, to identify communities in signed networks, we exploit a new greedy algorithm, taking signs and the density of these links into account. The main idea of the algorithm is the initial procedure of signed modularity and the corresponding update rules. Specially, we employ the “Asymmetric and Constrained Belief Evolution” procedure to evaluate the optimal number of communities. According to the experimental results, the algorithm is proved to be able to run well. More specifically, the proposed algorithm is very efficient for these networks with medium size, both dense and sparse.

Mapping U.S. cattle shipment networks: Spatial and temporal patterns of trade communities from 2009 to 2011.

PubMed

Gorsich, Erin E; Luis, Angela D; Buhnerkempe, Michael G; Grear, Daniel A; Portacci, Katie; Miller, Ryan S; Webb, Colleen T

2016-11-01

The application of network analysis to cattle shipments broadens our understanding of shipment patterns beyond pairwise interactions to the network as a whole. Such a quantitative description of cattle shipments in the U.S. can identify trade communities, describe temporal shipment patterns, and inform the design of disease surveillance and control strategies. Here, we analyze a longitudinal dataset of beef and dairy cattle shipments from 2009 to 2011 in the United States to characterize communities within the broader cattle shipment network, which are groups of counties that ship mostly to each other. Because shipments occur over time, we aggregate the data at various temporal scales to examine the consistency of network and community structure over time. Our results identified nine large (>50 counties) communities based on shipments of beef cattle in 2009 aggregated into an annual network and nine large communities based on shipments of dairy cattle. The size and connectance of the shipment network was highly dynamic; monthly networks were smaller than yearly networks and revealed seasonal shipment patterns consistent across years. Comparison of the shipment network over time showed largely consistent shipping patterns, such that communities identified on annual networks of beef and diary shipments from 2009 still represented 41-95% of shipments in monthly networks from 2009 and 41-66% of shipments from networks in 2010 and 2011. The temporal aspects of cattle shipments suggest that future applications of the U.S. cattle shipment network should consider seasonal shipment patterns. However, the consistent within-community shipping patterns indicate that yearly communities could provide a reasonable way to group regions for management. Copyright © 2016 Elsevier B.V. All rights reserved.

Approximation of Nash equilibria and the network community structure detection problem

PubMed Central

2017-01-01

Game theory based methods designed to solve the problem of community structure detection in complex networks have emerged in recent years as an alternative to classical and optimization based approaches. The Mixed Nash Extremal Optimization uses a generative relation for the characterization of Nash equilibria to identify the community structure of a network by converting the problem into a non-cooperative game. This paper proposes a method to enhance this algorithm by reducing the number of payoff function evaluations. Numerical experiments performed on synthetic and real-world networks show that this approach is efficient, with results better or just as good as other state-of-the-art methods. PMID:28467496

LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks

NASA Astrophysics Data System (ADS)

Berahmand, Kamal; Bouyer, Asgarali

2018-03-01

Community detection is an essential approach for analyzing the structural and functional properties of complex networks. Although many community detection algorithms have been recently presented, most of them are weak and limited in different ways. Label Propagation Algorithm (LPA) is a well-known and efficient community detection technique which is characterized by the merits of nearly-linear running time and easy implementation. However, LPA has some significant problems such as instability, randomness, and monster community detection. In this paper, an algorithm, namely node’s label influence policy for label propagation algorithm (LP-LPA) was proposed for detecting efficient community structures. LP-LPA measures link strength value for edges and nodes’ label influence value for nodes in a new label propagation strategy with preference on link strength and for initial nodes selection, avoid of random behavior in tiebreak states, and efficient updating order and rule update. These procedures can sort out the randomness issue in an original LPA and stabilize the discovered communities in all runs of the same network. Experiments on synthetic networks and a wide range of real-world social networks indicated that the proposed method achieves significant accuracy and high stability. Indeed, it can obviously solve monster community problem with regard to detecting communities in networks.

Hypothesis generation using network structures on community health center cancer-screening performance.

PubMed

Carney, Timothy Jay; Morgan, Geoffrey P; Jones, Josette; McDaniel, Anna M; Weaver, Michael T; Weiner, Bryan; Haggstrom, David A

2015-10-01

Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. This virtual experiment revealed that patterns of overall network symmetry, agent

Network Exposure and Homicide Victimization in an African American Community

PubMed Central

Wildeman, Christopher

2014-01-01

Objectives. We estimated the association of an individual’s exposure to homicide in a social network and the risk of individual homicide victimization across a high-crime African American community. Methods. Combining 5 years of homicide and police records, we analyzed a network of 3718 high-risk individuals that was created by instances of co-offending. We used logistic regression to model the odds of being a gunshot homicide victim by individual characteristics, network position, and indirect exposure to homicide. Results. Forty-one percent of all gun homicides occurred within a network component containing less than 4% of the neighborhood’s population. Network-level indicators reduced the association between individual risk factors and homicide victimization and improved the overall prediction of individual victimization. Network exposure to homicide was strongly associated with victimization: the closer one is to a homicide victim, the greater the risk of victimization. Regression models show that exposure diminished with social distance: each social tie removed from a homicide victim decreased one’s odds of being a homicide victim by 57%. Conclusions. Risk of homicide in urban areas is even more highly concentrated than previously thought. We found that most of the risk of gun violence was concentrated in networks of identifiable individuals. Understanding these networks may improve prediction of individual homicide victimization within disadvantaged communities. PMID:24228655

Community-aware task allocation for social networked multiagent systems.

PubMed

Wang, Wanyuan; Jiang, Yichuan

2014-09-01

In this paper, we propose a novel community-aware task allocation model for social networked multiagent systems (SN-MASs), where the agent' cooperation domain is constrained in community and each agent can negotiate only with its intracommunity member agents. Under such community-aware scenarios, we prove that it remains NP-hard to maximize system overall profit. To solve this problem effectively, we present a heuristic algorithm that is composed of three phases: 1) task selection: select the desirable task to be allocated preferentially; 2) allocation to community: allocate the selected task to communities based on a significant task-first heuristics; and 3) allocation to agent: negotiate resources for the selected task based on a nonoverlap agent-first and breadth-first resource negotiation mechanism. Through the theoretical analyses and experiments, the advantages of our presented heuristic algorithm and community-aware task allocation model are validated. 1) Our presented heuristic algorithm performs very closely to the benchmark exponential brute-force optimal algorithm and the network flow-based greedy algorithm in terms of system overall profit in small-scale applications. Moreover, in the large-scale applications, the presented heuristic algorithm achieves approximately the same overall system profit, but significantly reduces the computational load compared with the greedy algorithm. 2) Our presented community-aware task allocation model reduces the system communication cost compared with the previous global-aware task allocation model and improves the system overall profit greatly compared with the previous local neighbor-aware task allocation model.

Effect of source tampering in the security of quantum cryptography

NASA Astrophysics Data System (ADS)

Sun, Shi-Hai; Xu, Feihu; Jiang, Mu-Sheng; Ma, Xiang-Chun; Lo, Hoi-Kwong; Liang, Lin-Mei

2015-08-01

The security of source has become an increasingly important issue in quantum cryptography. Based on the framework of measurement-device-independent quantum key distribution (MDI-QKD), the source becomes the only region exploitable by a potential eavesdropper (Eve). Phase randomization is a cornerstone assumption in most discrete-variable (DV) quantum communication protocols (e.g., QKD, quantum coin tossing, weak-coherent-state blind quantum computing, and so on), and the violation of such an assumption is thus fatal to the security of those protocols. In this paper, we show a simple quantum hacking strategy, with commercial and homemade pulsed lasers, by Eve that allows her to actively tamper with the source and violate such an assumption, without leaving a trace afterwards. Furthermore, our attack may also be valid for continuous-variable (CV) QKD, which is another main class of QKD protocol, since, excepting the phase random assumption, other parameters (e.g., intensity) could also be changed, which directly determine the security of CV-QKD.

Community (in) Colleges: The Relationship Between Online Network Involvement and Academic Outcomes at a Community College

ERIC Educational Resources Information Center

Evans, Eliza D.; McFarland, Daniel A.; Rios-Aguilar, Cecilia; Deil-Amen, Regina

2016-01-01

Objective: This study explores the relationship between online social network involvement and academic outcomes among community college students. Prior theory hypothesizes that socio-academic moments are especially important for the integration of students into community colleges and that integration is related to academic outcomes. Online social…

The community structure of the European network of interlocking directorates 2005-2010.

PubMed

Heemskerk, Eelke M; Daolio, Fabio; Tomassini, Marco

2013-01-01

The boards of directors at large European companies overlap with each other to a sizable extent both within and across national borders. This could have important economic, political and management consequences. In this work we study in detail the topological structure of the networks that arise from this phenomenon. Using a comprehensive information database, we reconstruct the implicit networks of shared directorates among the top 300 European firms in 2005 and 2010, and suggest a number of novel ways to explore the trans-nationality of such business elite networks. Powerful community detection heuristics indicate that geography still plays an important role: there exist clear communities and they have a distinct national character. Nonetheless, from 2005 to 2010 we observe a densification of the boards interlocks network and a larger transnational orientation in its communities. Together with central actors and assortativity analyses, we provide statistical evidence that, at the level of corporate governance, Europe is getting closer.

Leveraging health social networking communities in translational research.

PubMed

Webster, Yue W; Dow, Ernst R; Koehler, Jacob; Gudivada, Ranga C; Palakal, Mathew J

2011-08-01

Health social networking communities are emerging resources for translational research. We have designed and implemented a framework called HyGen, which combines Semantic Web technologies, graph algorithms and user profiling to discover and prioritize novel associations across disciplines. This manuscript focuses on the key strategies developed to overcome the challenges in handling patient-generated content in Health social networking communities. Heuristic and quantitative evaluations were carried out in colorectal cancer. The results demonstrate the potential of our approach to bridge silos and to identify hidden links among clinical observations, drugs, genes and diseases. In Amyotrophic Lateral Sclerosis case studies, HyGen has identified 15 of the 20 published disease genes. Additionally, HyGen has highlighted new candidates for future investigations, as well as a scientifically meaningful connection between riluzole and alcohol abuse. Copyright © 2011 Elsevier Inc. All rights reserved.

Virality Prediction and Community Structure in Social Networks

NASA Astrophysics Data System (ADS)

Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

2013-08-01

How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

Virality Prediction and Community Structure in Social Networks

PubMed Central

Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

2013-01-01

How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications. PMID:23982106

Virality prediction and community structure in social networks.

PubMed

Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

2013-01-01

How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

Electronic community: The role of an electronic network in the development of a community of teachers engaged in curriculum development and implementation

NASA Astrophysics Data System (ADS)

Keating, Thomas Michael

The goal of this study was to describe the development of an electronic community of teachers who had the common experience of working on a Human Biology Curriculum Project through Stanford University. It was hypothesized that the interdisciplinary teams of teachers distributed across the United States would find a telecommunication network an ideal vehicle for extending their curricular collaborations they had begun in a series of summer institutes at Stanford. It was antlclpated that teachers would use the network to keep in touch with each other, share their common experiences piloting the HumBio Curriculum materials, provide feedback to the faculty and staff writing teams, and explore the possibilities of enacting cross site projects based on the curriculum project. From these interactions over the network it was hypothesized that a viable electronic community of schools could emerge. Establishment of a thriving electronic educational community is not an easy task. An analysis of three years of network interactions representing approximately 3125 email messages exchanged between HumBio test sites, HumBio Staff, the Network Coordinator and an additional three schools added in the third year, did not support the hypothesis that an electronic community would emerge and prosper. Participation in the electronic network was largely sporadic. However, a core group of schools was able to engage in meaningful, long term, cross-site projects, and student exchanges. By studying the active schools' message exchanges through time, insights were gained as to which ingredients are necessary to nurture an electronic network through the early stages of community development. A life history approach was found to be useful when considering the developmental stages of electronic networks. A key finding is that teachers choose to participate in electronic collaborations that will have a direct impact on what students are doing in the classroom. The first phase in the development of

Investigating student communities with network analysis of interactions in a physics learning center

NASA Astrophysics Data System (ADS)

Brewe, Eric; Kramer, Laird; Sawtelle, Vashti

2012-06-01

Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC) that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

NASA Astrophysics Data System (ADS)

Perotti, Juan Ignacio; Tessone, Claudio Juan; Caldarelli, Guido

2015-12-01

The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust, and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the hierarchical mutual information, which is a generalization of the traditional mutual information and makes it possible to compare hierarchical partitions and hierarchical community structures. The normalized version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies and on the hierarchical community structure of artificial and empirical networks. Furthermore, the experiments illustrate some of the practical applications of the hierarchical mutual information, namely the comparison of different community detection methods and the study of the consistency, robustness, and temporal evolution of the hierarchical modular structure of networks.

The development of a network for community-based obesity prevention: the CO-OPS Collaboration

PubMed Central

2011-01-01

Background Community-based interventions are a promising approach and an important component of a comprehensive response to obesity. In this paper we describe the Collaboration of COmmunity-based Obesity Prevention Sites (CO-OPS Collaboration) in Australia as an example of a collaborative network to enhance the quality and quantity of obesity prevention action at the community level. The core aims of the CO-OPS Collaboration are to: identify and analyse the lessons learned from a range of community-based initiatives aimed at tackling obesity, and; to identify the elements that make community-based obesity prevention initiatives successful and share the knowledge gained with other communities. Methods Key activities of the collaboration to date have included the development of a set of Best Practice Principles and knowledge translation and exchange activities to promote the application (or use) of evidence, evaluation and analysis in practice. Results The establishment of the CO-OPS Collaboration is a significant step toward strengthening action in this area, by bringing together research, practice and policy expertise to promote best practice, high quality evaluation and knowledge translation and exchange. Future development of the network should include facilitation of further evidence generation and translation drawing from process, impact and outcome evaluation of existing community-based interventions. Conclusions The lessons presented in this paper may help other networks like CO-OPS as they emerge around the globe. It is important that networks integrate with each other and share the experience of creating these networks. PMID:21349185

The GÉANT network: addressing current and future needs of the HEP community

NASA Astrophysics Data System (ADS)

Capone, Vincenzo; Usman, Mian

2015-12-01

The GÉANT infrastructure is the backbone that serves the scientific communities in Europe for their data movement needs and their access to international research and education networks. Using the extensive fibre footprint and infrastructure in Europe the GÉANT network delivers a portfolio of services aimed to best fit the specific needs of the users, including Authentication and Authorization Infrastructure, end-to-end performance monitoring, advanced network services (dynamic circuits, L2-L3VPN, MD-VPN). This talk will outline the factors that help the GÉANT network to respond to the needs of the High Energy Physics community, both in Europe and worldwide. The Pan-European network provides the connectivity between 40 European national research and education networks. In addition, GÉANT also connects the European NRENs to the R&E networks in other world region and has reach to over 110 NREN worldwide, making GÉANT the best connected Research and Education network, with its multiple intercontinental links to different continents e.g. North and South America, Africa and Asia-Pacific. The High Energy Physics computational needs have always had (and will keep having) a leading role among the scientific user groups of the GÉANT network: the LHCONE overlay network has been built, in collaboration with the other big world REN, specifically to address the peculiar needs of the LHC data movement. Recently, as a result of a series of coordinated efforts, the LHCONE network has been expanded to the Asia-Pacific area, and is going to include some of the main regional R&E network in the area. The LHC community is not the only one that is actively using a distributed computing model (hence the need for a high-performance network); new communities are arising, as BELLE II. GÉANT is deeply involved also with the BELLE II Experiment, to provide full support to their distributed computing model, along with a perfSONAR-based network monitoring system. GÉANT has also

Convergent evolution of modularity in metabolic networks through different community structures.

PubMed

Zhou, Wanding; Nakhleh, Luay

2012-09-14

It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxonomy. We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organism's metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new measures of modularity and network

Convergent evolution of modularity in metabolic networks through different community structures

PubMed Central

2012-01-01

Background It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. Results In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxomony. Conclusions We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organism’s metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new

Exploring anti-community structure in networks with application to incompatibility of traditional Chinese medicine

NASA Astrophysics Data System (ADS)

Zhu, Jiajing; Liu, Yongguo; Zhang, Yun; Liu, Xiaofeng; Xiao, Yonghua; Wang, Shidong; Wu, Xindong

2017-11-01

Community structure is one of the most important properties in networks, in which a node shares its most connections with the others in the same community. On the contrary, the anti-community structure means the nodes in the same group have few or no connections with each other. In Traditional Chinese Medicine (TCM), the incompatibility problem of herbs is a challenge to the clinical medication safety. In this paper, we propose a new anti-community detection algorithm, Random non-nEighboring nOde expansioN (REON), to find anti-communities in networks, in which a new evaluation criterion, anti-modularity, is designed to measure the quality of the obtained anti-community structure. In order to establish anti-communities in REON, we expand the node set by non-neighboring node expansion and regard the node set with the highest anti-modularity as an anti-community. Inspired by the phenomenon that the node with higher degree has greater contribution to the anti-modularity, an improved algorithm called REONI is developed by expanding node set by the non-neighboring node with the maximum degree, which greatly enhances the efficiency of REON. Experiments on synthetic and real-world networks demonstrate the superiority of the proposed algorithms over the existing methods. In addition, by applying REONI to the herb network, we find that it can discover incompatible herb combinations.

[Policy networks combating hunger and poverty: the Solidarity Community strategy in Brazil].

PubMed

Burlandy, Luciene; Labra, Maria Eliana

2007-01-01

This paper analyzes a strategy deployed by the Brazilian Government for combating hunger and poverty: the Solidarity Community (1995-2003), particularly institutional mechanisms used to fine-tune targeting processes and allocate resources to the Food Stocks Distribution Program (PRODEA) and the Undernourished Child and High-Risk Pregnancy Program (PCDMI). Primary data were obtained through interviews with policy network players, including segments of government and society: nine Federal; six State and 82 from eight Municipalities in Rio de Janeiro state. Moving towards its goal of converging programs for the poorest municipalities, the Solidarity Community made them more visible to executive civil servants. The introduction of different sectors into the Solidarity Community network varied, according to the political clout and institutional capacity of each sector. The Solidarity Community strategy was: to negotiate criteria with Ministries for setting priorities and provide technical support and information for local governments, improving their skills for obtaining federal funding. The role of the Solidarity Community was thus limited at the local level, due to poor intersectoral networking and difficulties in monitoring program implementation and beneficiary selection processes, blunting its advantages for more vulnerable groups.

Multiplex social ecological network analysis reveals how social changes affect community robustness more than resource depletion.

PubMed

Baggio, Jacopo A; BurnSilver, Shauna B; Arenas, Alex; Magdanz, James S; Kofinas, Gary P; De Domenico, Manlio

2016-11-29

Network analysis provides a powerful tool to analyze complex influences of social and ecological structures on community and household dynamics. Most network studies of social-ecological systems use simple, undirected, unweighted networks. We analyze multiplex, directed, and weighted networks of subsistence food flows collected in three small indigenous communities in Arctic Alaska potentially facing substantial economic and ecological changes. Our analysis of plausible future scenarios suggests that changes to social relations and key households have greater effects on community robustness than changes to specific wild food resources.

Model of community emergence in weighted social networks

NASA Astrophysics Data System (ADS)

Kumpula, J. M.; Onnela, J.-P.; Saramäki, J.; Kertész, J.; Kaski, K.

2009-04-01

Over the years network theory has proven to be rapidly expanding methodology to investigate various complex systems and it has turned out to give quite unparalleled insight to their structure, function, and response through data analysis, modeling, and simulation. For social systems in particular the network approach has empirically revealed a modular structure due to interplay between the network topology and link weights between network nodes or individuals. This inspired us to develop a simple network model that could catch some salient features of mesoscopic community and macroscopic topology formation during network evolution. Our model is based on two fundamental mechanisms of network sociology for individuals to find new friends, namely cyclic closure and focal closure, which are mimicked by local search-link-reinforcement and random global attachment mechanisms, respectively. In addition we included to the model a node deletion mechanism by removing all its links simultaneously, which corresponds for an individual to depart from the network. Here we describe in detail the implementation of our model algorithm, which was found to be computationally efficient and produce many empirically observed features of large-scale social networks. Thus this model opens a new perspective for studying such collective social phenomena as spreading, structure formation, and evolutionary processes.

Providing interoperability of eHealth communities through peer-to-peer networks.

PubMed

Kilic, Ozgur; Dogac, Asuman; Eichelberg, Marco

2010-05-01

Providing an interoperability infrastructure for Electronic Healthcare Records (EHRs) is on the agenda of many national and regional eHealth initiatives. Two important integration profiles have been specified for this purpose, namely, the "Integrating the Healthcare Enterprise (IHE) Cross-enterprise Document Sharing (XDS)" and the "IHE Cross Community Access (XCA)." IHE XDS describes how to share EHRs in a community of healthcare enterprises and IHE XCA describes how EHRs are shared across communities. However, the current version of the IHE XCA integration profile does not address some of the important challenges of cross-community exchange environments. The first challenge is scalability. If every community that joins the network needs to connect to every other community, i.e., a pure peer-to-peer network, this solution will not scale. Furthermore, each community may use a different coding vocabulary for the same metadata attribute, in which case, the target community cannot interpret the query involving such an attribute. Yet another important challenge is that each community may (and typically will) have a different patient identifier domain. Querying for the patient identifiers in the target community using patient demographic data may create patient privacy concerns. In this paper, we address each of these challenges and show how they can be handled effectively in a superpeer-based peer-to-peer architecture.

How plants connect pollination and herbivory networks and their contribution to community stability.

PubMed

Sauve, Alix M C; Thébault, Elisa; Pocock, Michael J O; Fontaine, Colin

2016-04-01

Pollination and herbivory networks have mainly been studied separately, highlighting their distinct structural characteristics and the related processes and dynamics. However, most plants interact with both pollinators and herbivores, and there is evidence that both types of interaction affect each other. Here we investigated the way plants connect these mutualistic and antagonistic networks together, and the consequences for community stability. Using an empirical data set, we show that the way plants connect pollination and herbivory networks is not random and promotes community stability. Analyses of the structure of binary and quantitative networks show different results: the plants' generalism with regard to pollinators is positively correlated to their generalism with regard to herbivores when considering binary interactions, but not when considering quantitative interactions. We also show that plants that share the same pollinators do not share the same herbivores. However, the way plants connect pollination and herbivory networks promotes stability for both binary and quantitative networks. Our results highlight the relevance of considering the diversity of interaction types in ecological communities, and stress the need to better quantify the costs and benefits of interactions, as well as to develop new metrics characterizing the way different interaction types are combined within ecological networks.

Social Networks, Communication Styles, and Learning Performance in a CSCL Community

ERIC Educational Resources Information Center

Cho, Hichang; Gay, Geri; Davidson, Barry; Ingraffea, Anthony

2007-01-01

The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning…

Fungal networks shape dynamics of bacterial dispersal and community assembly in cheese rind microbiomes.

PubMed

Zhang, Yuanchen; Kastman, Erik K; Guasto, Jeffrey S; Wolfe, Benjamin E

2018-01-23

Most studies of bacterial motility have examined small-scale (micrometer-centimeter) cell dispersal in monocultures. However, bacteria live in multispecies communities, where interactions with other microbes may inhibit or facilitate dispersal. Here, we demonstrate that motile bacteria in cheese rind microbiomes use physical networks created by filamentous fungi for dispersal, and that these interactions can shape microbial community structure. Serratia proteamaculans and other motile cheese rind bacteria disperse on fungal networks by swimming in the liquid layers formed on fungal hyphae. RNA-sequencing, transposon mutagenesis, and comparative genomics identify potential genetic mechanisms, including flagella-mediated motility, that control bacterial dispersal on hyphae. By manipulating fungal networks in experimental communities, we demonstrate that fungal-mediated bacterial dispersal can shift cheese rind microbiome composition by promoting the growth of motile over non-motile community members. Our single-cell to whole-community systems approach highlights the interactive dynamics of bacterial motility in multispecies microbiomes.

Analysis of the social network development of a virtual community for Australian intensive care professionals.

PubMed

Rolls, Kaye Denise; Hansen, Margaret; Jackson, Debra; Elliott, Doug

2014-11-01

Social media platforms can create virtual communities, enabling healthcare professionals to network with a broad range of colleagues and facilitate knowledge exchange. In 2003, an Australian state health department established an intensive care mailing list to address the professional isolation experienced by senior intensive care nurses. This article describes the social network created within this virtual community by examining how the membership profile evolved from 2003 to 2009. A retrospective descriptive design was used. The data source was a deidentified member database. Since 2003, 1340 healthcare professionals subscribed to the virtual community with 78% of these (n = 1042) still members at the end of 2009. The membership profile has evolved from a single-state nurse-specific network to an Australia-wide multidisciplinary and multiorganizational intensive care network. The uptake and retention of membership by intensive care clinicians indicated that they appeared to value involvement in this virtual community. For healthcare organizations, a virtual community may be a communications option for minimizing professional and organizational barriers and promoting knowledge flow. Further research is, however, required to demonstrate a link between these broader social networks, enabling the exchange of knowledge and improved patient outcomes.

Loneliness, social support networks, mood and wellbeing in community-dwelling elderly.

PubMed

Golden, Jeannette; Conroy, Ronán M; Bruce, Irene; Denihan, Aisling; Greene, Elaine; Kirby, Michael; Lawlor, Brian A

2009-07-01

Both loneliness and social networks have been linked with mood and wellbeing. However, few studies have examined these factors simultaneously in community-dwelling participants. The aim of this study was to examine the relationship between social network, loneliness, depression, anxiety and quality of life in community dwelling older people living in Dublin. One thousand two hundred and ninety-nine people aged 65 and over, recruited through primary care practices, were interviewed in their own homes using the GMS-AGECAT. Social network was assessed using Wenger's typology. 35% of participants were lonely, with 9% describing it as painful and 6% as intrusive. Similarly, 34% had a non-integrated social network. However, the two constructs were distinct: 32% of participants with an integrated social network reported being lonely. Loneliness was higher in women, the widowed and those with physical disability and increased with age, but when age-related variables were controlled for this association was non-significant. Wellbeing, depressed mood and hopelessness were all independently associated with both loneliness and non-integrated social network. In particular, loneliness explained the excess risk of depression in the widowed. The population attributable risk (PAR) associated with loneliness was 61%, compared with 19% for non-integrated social network. Taken together they had a PAR of 70% Loneliness and social networks both independently affect mood and wellbeing in the elderly, underlying a very significant proportion of depressed mood.

Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0

PubMed Central

Wang, Yan; DeLisi, Charles; Segrè, Daniel; Hu, Zhenjun

2016-01-01

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT’s unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the “symbiotic layout” of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu. PMID:27081850

Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

PubMed

Granger, Brian R; Chang, Yi-Chien; Wang, Yan; DeLisi, Charles; Segrè, Daniel; Hu, Zhenjun

2016-04-01

The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

Community, Collective or Movement? Evaluating Theoretical Perspectives on Network Building

NASA Astrophysics Data System (ADS)

Spitzer, W.

2015-12-01

Since 2007, the New England Aquarium has led a national effort to increase the capacity of informal science venues to effectively communicate about climate change. We are now leading the NSF-funded National Network for Ocean and Climate Change Interpretation (NNOCCI), partnering with the Association of Zoos and Aquariums, FrameWorks Institute, Woods Hole Oceanographic Institution, Monterey Bay Aquarium, and National Aquarium, with evaluation conducted by the New Knowledge Organization, Pennsylvania State University, and Ohio State University. NNOCCI enables teams of informal science interpreters across the country to serve as "communication strategists" - beyond merely conveying information they can influence public perceptions, given their high level of commitment, knowledge, public trust, social networks, and visitor contact. We provide in-depth training as well as an alumni network for ongoing learning, implementation support, leadership development, and coalition building. Our goals are to achieve a systemic national impact, embed our work within multiple ongoing regional and national climate change education networks, and leave an enduring legacy. What is the most useful theoretical model for conceptualizing the work of the NNOCCI community? This presentation will examine the pros and cons of three perspectives -- community of practice, collective impact, and social movements. The community of practice approach emphasizes use of common tools, support for practice, social learning, and organic development of leadership. A collective impact model focuses on defining common outcomes, aligning activities toward a common goal, structured collaboration. A social movement emphasizes building group identity and creating a sense of group efficacy. This presentation will address how these models compare in terms of their utility in program planning and evaluation, their fit with the unique characteristics of the NNOCCI community, and their relevance to our program goals.

The Community Structure of the European Network of Interlocking Directorates 2005–2010