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.
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References
Valente, T.W.: Social Networks and Health: Models, Methods, and Applications. Oxford University Press, New York (2010)
Freeman, L.C.: Centrality in social networks conceptual clarification. Social Netw. 1(3), 215–239 (1978)
Newman, M.E.: The mathematics of networks. New Palgrave Encycl. Econ. 2(2008), 1–12 (2008)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008(10), P10008 (2008)
Bastian, M., Heymann, S., Jacomy, M., et al.: Gephi: an open source software for exploring and manipulating networks. Icwsm 8(2009), 361–362 (2009)
Marsden, P.V., Friedkin, N.E.: Network studies of social influence. Sociol. Methods Res. 22(1), 127–151 (1993)
Burt, R.S.: Social contagion and innovation: cohesion versus structural equivalence. Am. J. Sociol. 92(6), 1287–1335 (1987)
Valente, T.W.: Network models of the diffusion of innovations. Comput. Math. Organ. Theory 2(2), 163–164 (1996)
Valente, T.W.: Network models and methods for studying the diffusion of innovations. Models Methods Soc. Netw. Anal. 28, 98 (2005)
Rogers, E.M.: Diffusion of Innovations. Simon and Schuster, New York (2010)
Leenders, R.T.A.: Modeling social influence through network autocorrelation: constructing the weight matrix. Soc. Netw. 24(1), 21–47 (2002)
Fujimoto, K., Chou, C.P., Valente, T.W.: The network autocorrelation model using two-mode data: affiliation exposure and potential bias in the autocorrelation parameter. Soc. Netw. 33(3), 231–243 (2011)
Fujimoto, K., Unger, J.B., Valente, T.W.: A network method of measuring affiliation-based peer influence: assessing the influences of teammates’ smoking on adolescent smoking. Child Dev. 83(2), 442–451 (2012)
Papachristos, A.V., Wildeman, C., Roberto, E.: Tragic, but not random: the social contagion of nonfatal gunshot injuries. Soc. Sci. Med. 125, 139–150 (2015)
Wipfli, H.L., Fujimoto, K., Valente, T.W.: Global tobacco control diffusion: the case of the framework convention on tobacco control. Am. J. Public Health 100(7), 1260–1266 (2010)
Myneni, S., Fujimoto, K., Cobb, N., Cohen, T.: Content-driven analysis of an online community for smoking cessation: integration of qualitative techniques, automated text analysis, and affiliation networks. Am. J. Public Health 105(6), 1206–1212 (2015)
Fujimoto, K., Wang, P., Valente, T.W.: The decomposed affiliation exposure model: a network approach to segregating peer influences from crowds and organized sports. Netw. Sci. 1(2), 154–169 (2013)
Fujimoto, K., Valente, T.W.: Alcohol peer influence of participating in organized school activities: a network approach. Health Psychol. 32(10), 1084 (2013)
Rocha, L.E.C., Liljeros, F., Holme, P.: Information dynamics shape the sexual networks of Internet-mediated prostitution. Proc. Natl. Acad. Sci. U.S.A. 107(13), 5706–5711 (2010)
Rogers, E.M., Kincaid, D.L.: Communication networks: toward a new paradigm for research (1981)
West, P., Sweeting, H.: Background, rationale and design of the west of scotland 11 to 16 study. MRC Medical Sociology Unit Working Paper, no. 52 (1996)
Michell, L., Amos, A.: Girls, pecking order and smoking. Soc. Sci. Med. 44(12), 1861–1869 (1997)
Michell, M.P.L.: Smoke rings: social network analysis of friendship groups, smoking and drug-taking. Drugs: Educ. Prevent. Policy 7(1), 21–37 (2000)
Pearson, M., West, P.: Drifting smoke rings. Connections 25(2), 59–76 (2003)
Yon, G.V., et al.: netdiffuser: Network analysis for diffusion of innovations (2016)
Fujimoto, K.: AFFILIATIONEXPOSURE: stata module to compute an affiliation exposure model using two-mode actor(row)-by-event(column) network data. Statistical Software Components, Boston College Department of Economics, June 2011
Valente, T.W., Dyal, S.R., Chu, K.H., Wipfli, H., Fujimoto, K.: Diffusion of innovations theory applied to global tobacco control treaty ratification. Soc. Sci. Med. 145, 89–97 (2015)
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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Amith, M.“., Fujimoto, K., Tao, C. (2019). NET-EXPO: A Gephi Plugin Towards Social Network Analysis of Network Exposure for Unipartite and Bipartite Graphs. In: Stephanidis, C. (eds) HCI International 2019 - Posters. HCII 2019. Communications in Computer and Information Science, vol 1034. Springer, Cham. https://doi.org/10.1007/978-3-030-23525-3_1
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