Adding Temporal Dimension in Social Network by Using Link Analysis

2017 ◽  
Author(s):  
Farhad Muhammad Riaz ◽  
Rashid Abbasi ◽  
Zahid Mahmood
Keyword(s):  
Social Network ◽  
Link Analysis ◽  
Temporal Dimension

Stable Communities Detection Method for Temporal Multiplex Graphs: Heterogeneous Social Network Case Study

2020 ◽  
Author(s):  
Wala Rebhi ◽  
Nesrine Ben Yahia ◽  
Narjès Bellamine Ben Saoud
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Detection Algorithm ◽  
Step Method ◽  
Temporal Dimension ◽  
Temporal Aspect ◽  
Heterogeneous Social Networks ◽  
Community Detection Algorithm ◽  
High Level ◽  
Temporal Graphs

Abstract Multiplex graphs have been recently proposed as a model to represent high-level complexity in real-world networks such as heterogeneous social networks where actors could be characterized by heterogeneous properties and could be linked with different types of social interactions. This has brought new challenges in community detection, which aims to identify pertinent groups of nodes in a complex graph. In this context, great efforts have been made to tackle the problem of community detection in multiplex graphs. However, most of the proposed methods until recently deal with static multiplex graph and ignore the temporal dimension, which is a key characteristic of real networks. Even more, the few methods that consider temporal graphs, they just propose to follow communities over time and none of them use the temporal aspect directly to detect stable communities, which are often more meaningful in reality. Thus, this paper proposes a new two-step method to detect stable communities in temporal multiplex graphs. The first step aims to find the best static graph partition at each instant by applying a new hybrid community detection algorithm, which considers both relations heterogeneities and nodes similarities. Then, the second step considers the temporal dimension in order to find final stable communities. Finally, experiments on synthetic graphs and a real social network show that this method is competitive and it is able to extract high-quality communities.

Visualization of the Nordic academic web: Link analysis using social network tools

2008 ◽  
Vol 44 (4) ◽  
pp. 1624-1633 ◽  
Author(s):  
José Luis Ortega ◽  
Isidro F. Aguillo
Keyword(s):  
Social Network ◽  
Link Analysis

scholarly journals Diffusion dynamics of influence in a social network of intellectuals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Raji Ghawi ◽  
Cindarella Petz ◽  
Jürgen Pfeffer
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Temporal Dimension ◽  
Diffusion Dynamics ◽  
History Of ◽  
Over Time

AbstractThe history of intellectuals consists of a complex web of influences and interconnections of philosophers, scientists, writers, their work, and ideas. To understand how did these influences evolve over time, we mined a network of influence of over 12,500 intellectuals, enriched it with a temporal dimension dividing the history into six eras. We analyze time-sliced projections of the network into within-era, inter-era, and accumulated-era networks, and identify various patterns of intellectuals and eras and studied their development in time. We also construct influence cascades, analyze their properties: size, depth and breadth, and analyze how the cascades of influence evolve over the consecutive eras. We find out that the cascades are clustered into two categories, namely small- and large cascades. An interesting finding here is that the fraction of small cascades increases, while the fraction of larges cascades decreases over time. We also briefly analyze the community structure within the influence network of scholars.

Using Modern Social Network Techniques to Expand Link Analysis in a Nuclear Reactor Console Redesign

2016 ◽  
Vol 60 (1) ◽  
pp. 1083-1087 ◽  
Author(s):  
Swaroop Dinakar ◽  
Kathryn G. Tippey ◽  
Trey Roady ◽  
Julien Edery ◽  
Thomas K. Ferris
Keyword(s):  
Social Network ◽  
Critical Incident ◽  
Nuclear Reactor ◽  
Process Analysis ◽  
Current System ◽  
Link Analysis ◽  
Control Panel ◽  
Hierarchical Task Analysis ◽  
Factors Analysis ◽  
Centrality Analysis

As part of the Nuclear Regulatory Commission’s recertification of Texas A&M University’s AGN-201M nuclear reactor, a human factors analysis was performed to evaluate the drawbacks of the current system and make design recommendations for a new console layout. The process involved three phases. Background development consisted of a literature review and expert interviews (both structured and unstructured). Process analysis was performed using hierarchical task analysis, critical incident analysis, and heuristic usability walkthroughs. Control panel redesign utilized an expanded version of link analysis through adding modern social networking analysis techniques. While social network analysis has previously been used for design, particular emphasis in this paper is placed on the novel application of faction and centrality analysis to identify group categories for console redesign.

The social network

2013 ◽  
Vol 44 (2) ◽  
pp. 22
Author(s):  
ALAN ROCKOFF
Keyword(s):  
Social Network ◽  
The Social

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

Extraverts Have Larger Social Network Layers

2011 ◽  
Vol 32 (3) ◽  
pp. 161-169 ◽  
Author(s):  
Thomas V. Pollet ◽  
Sam G. B. Roberts ◽  
Robin I. M. Dunbar
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Outer Layer ◽  
Network Size ◽  
Emotional Intensity ◽  
Emotional Closeness ◽  
Network Layer ◽  
Network Layers ◽  
Social Network Size

Previous studies showed that extraversion influences social network size. However, it is unclear how extraversion affects the size of different layers of the network, and how extraversion relates to the emotional intensity of social relationships. We examined the relationships between extraversion, network size, and emotional closeness for 117 individuals. The results demonstrated that extraverts had larger networks at every layer (support clique, sympathy group, outer layer). The results were robust and were not attributable to potential confounds such as sex, though they were modest in size (raw correlations between extraversion and size of network layer, .20 < r < .23). However, extraverts were not emotionally closer to individuals in their network, even after controlling for network size. These results highlight the importance of considering not just social network size in relation to personality, but also the quality of relationships with network members.

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

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