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NET-EXPO: A Gephi Plugin Towards Social Network Analysis of Network Exposure for Unipartite and Bipartite Graphs

  • Muhammad “Tuan” Amith
  • Kayo Fujimoto
  • Cui TaoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1034)

Abstract

Social network analysis (SNA) concerns itself in studying network structures in relation to individuals’ behavior. Individuals may be influenced by their network members in their behavior, and thus past researchers have developed computational methods that allow us to measure the extent to which individuals are exposed to members with certain behavior within one’s social network, and that be correlated with their own behavior. Some of these methods include network exposure model, affiliation exposure model, and decomposed network exposure models. We developed a Gephi plugin that computes and visualizes these various kinds of network exposure models called NET-EXPO. We experimented with NET-EXPO on some social network datasets to demonstrate its pragmatic use in social network research. This plugin has the potential to equip researchers with a tool to compute network exposures in a user friendly way and simplify the process to compute and visualize the network data.

Keywords

Social network analysis Network exposure model Affiliation exposure model Visualization Gephi Human computer interaction Software Network science Public health 

Notes

Acknowledgments

This research was supported by the UTHealth Innovation for Cancer Prevention Research Training Program (Cancer Prevention and Research Institute of Texas grant # RP160015), the National Library of Medicine of the National Institutes of Health under Award Number R01LM011829, and the National Institute on Alcohol Abuse and Alcoholism (1K99AA019699), and the National Institute of Mental Health of the National Institutes of Health under Award Numbers R01MH100021.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Muhammad “Tuan” Amith
    • 1
  • Kayo Fujimoto
    • 1
  • Cui Tao
    • 1
    Email author
  1. 1.University of Texas Health Science Center at HoustonHoustonUSA

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