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Computation and analysis of temporal betweenness in a knowledge mobilization network.

Amir Afrasiabi Rad1, Paola Flocchini1, Joanne Gaudet2

  • 1School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario Canada.

Computational Social Networks
|December 22, 2017
PubMed
Summary

We introduce foremost betweenness, a new temporal measure for dynamic social networks. This method reveals hidden centrality roles, identifying key "accelerators" in knowledge mobilization networks that static analysis misses.

Keywords:
Dynamic networksSocial networksTemporal analysisTemporal betweennessTime-varying graphs

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Area of Science:

  • Network Science
  • Social Network Analysis
  • Information Science

Background:

  • Dynamic social networks with changing connectivity are increasingly common.
  • Knowledge mobilization networks exemplify dynamic social networks.
  • Traditional network analysis often overlooks the temporal dimension, using static representations and missing crucial temporal roles.

Purpose of the Study:

  • To address the limitation of static analysis in dynamic networks by proposing a temporal betweenness measure.
  • To introduce and develop a method for calculating 'foremost betweenness' to analyze temporal roles.
  • To apply this measure to a knowledge mobilization network to uncover time-dependent centrality.

Main Methods:

  • Developed an analytical algorithm to compute foremost betweenness.
  • Conducted an experimental application of the algorithm to a knowledge mobilization network case study.

Main Results:

  • Proposed and computed foremost betweenness, a novel temporal betweenness measure.
  • Identified nodes with negligible static centrality but significant temporal roles as accelerators.
  • Detected nodes with high static centrality but low temporal bridging importance.

Conclusions:

  • Foremost betweenness effectively reveals temporal roles, particularly 'accelerators,' in dynamic networks.
  • This temporal measure offers a more nuanced understanding of network dynamics than static analysis.
  • The study opens avenues for further research into the impact of time in social network analysis.