Computation and analysis of temporal betweenness in a knowledge mobilization network View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Amir Afrasiabi Rad, Paola Flocchini, Joanne Gaudet

ABSTRACT

Background: Highly dynamic social networks, where connectivity continuously changes in time, are becoming more and more pervasive. Knowledge mobilization, which refers to the use of knowledge toward the achievement of goals, is one of the many examples of dynamic social networks. Despite the wide use and extensive study of dynamic networks, their temporal component is often neglected in social network analysis, and statistical measures are usually performed on static network representations. As a result, measures of importance (like betweenness centrality) typically do not reveal the temporal role of the entities involved. Our goal is to contribute to fill this limitation by proposing a form of temporal betweenness measure (foremost betweenness). Methods: Our method is analytical as well as experimental: we design an algorithm to compute foremost betweenness, and we apply it to a case study to analyze a knowledge mobilization network. Results: We propose a form of temporal betweenness measure (foremost betweenness) to analyze a knowledge mobilization network and we introduce, for the first time, an algorithm to compute exact foremost betweenness. We then show that this measure, which explicitly takes time into account, allows us to detect centrality roles that were completely hidden in the classical statistical analysis. In particular, we uncover nodes whose static centrality was negligible, but whose temporal role might instead be important to accelerate mobilization flow in the network. We also observe the reverse behavior by detecting nodes with high static centrality, whose role as temporal bridges is instead very low. Conclusion: In this paper, we focus on a form of temporal betweenness designed to detect accelerators in dynamic networks. By revealing potentially important temporal roles, this study is a first step toward a better understanding of the impact of time in social networks and opens the road to further investigation. More... »

PAGES

5

References to SciGraph publications

Journal

TITLE

Computational Social Networks

ISSUE

1

VOLUME

4

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40649-017-0041-7

DOI

http://dx.doi.org/10.1186/s40649-017-0041-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1090681065

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29266139


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