Hostname: page-component-77c89778f8-m42fx Total loading time: 0 Render date: 2024-07-18T11:37:57.989Z Has data issue: false hasContentIssue false
  • English
  • Français

Identifying key players in bipartite networks

Published online by Cambridge University Press:  17 January 2020

Scott W. Duxbury Open the ORCID record for Scott W. Duxbury [Opens in a new window]
Scott W. Duxbury*
Affiliation:
Department of Sociology, University of North Carolina Chapel Hill, 155 Hamilton Hall, 102 Emerson Drive, Chapel Hill, NC27514, USA (email: duxbury.5@osu.edu)
Get access Rights & Permissions [Opens in a new window]

Abstract

Measures of bipartite network structure have recently gained attention from network scholars. However, there is currently no measure for identifying key players in two-mode networks. This article proposes measures for identifying key players in bipartite networks. It focuses on two measures: fragmentation and cohesion centrality. It extends the centrality measures to bipartite networks by considering (1) cohesion and fragmentation centrality within a one-mode projection, (2) cross-modal cohesion and fragmentation centrality, where a node in one mode is influential in the one-mode projection of the other mode, and (3) cohesion and fragmentation centrality across the entire bipartite structure. Empirical examples are provided for the Southern Women’s data and on the Ndrangheta mafia data.

Keywords

two-mode networkvulnerabilityinfluencecentralitydisruption
Type
Research Article
Information
Network Science , Volume 8 , Issue 1 , March 2020 , pp. 42 - 61
Copyright
© Cambridge University Press 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agneessens, F., Borgatti, S. P., & Everett, M. G. (2017). Geodesic based centrality: Unifying the local and the global. Social Networks, 49, 12–26.CrossRefGoogle Scholar
An, W., & Liu, Y.H. (2016). keyplayer: An R Package for locating key players in social networks. The R Journal, 8(1), 258–270.CrossRefGoogle Scholar
Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(1), 243–269.CrossRefGoogle Scholar
Borgatti, S. P. (2006). Identifying sets of key players in a social network. Computational and Mathematical Organizational Theory, 12(1), 21–34.CrossRefGoogle Scholar
Breiger, R. L. (1974). The duality of persons and groups. Social Forces, 53(2), 181–190.CrossRefGoogle Scholar
Chen, Y., Paul, G., Havlin, S., Liljeros, F., & Stanley, H. E. (2008). Finding a better immunization strategy. Physical Review Letters, 101, 058701.CrossRefGoogle ScholarPubMed
Cobb, N. K, Graham, A. L., & Abrams, D. B. (2010). Social network structure of a large online community for smoking cessation. American Journal of Public Health, 100(7), 1,282–1,289.CrossRefGoogle ScholarPubMed
Coutinho, J. (2016). Ndrangheta Mafia 2. UCINET Data Repository. Retrieved from https://sites.google.com/site/ucinetsoftware/datasets/covert-networks/ndranghetamafia2Google Scholar
Davis, A., Gardner, B. B., & Gardney, M. (1941). Deep South: A social anthropological study of caste and class. Chicago: University of Chicago Press.Google Scholar
Duijn, P. A. C., Kashirin, V., & Sloot, P. M. A. (2014). The relative ineffectiveness of criminal network disruption. Scientific Reports, 4, 4, 238.Google ScholarPubMed
Duxbury, S. W, & Haynie, D. L. (2019). Criminal network security: An agent-based approach to evaluating network resilience. Criminology, 57(2), 314–342.CrossRefGoogle Scholar
Everett, M. G., & Borgatti, S. P. (2013). The dual-projection approach for two-mode networks. Social Networks, 35(2), 204–210.CrossRefGoogle Scholar
Everett, M. G. (2016). Centrality and the dual-projection approach for two-mode social network data. Methodological Innovations, 9(1), 1–8.CrossRefGoogle Scholar
Fujimoto, K., Chou, C. P., & Valente, T. W. (2011). The network autocorrelation model using two-mode data: Affiliation exposure and potential bias in the autocorrelation parameter. Social Networks, 33(3), 231–243.CrossRefGoogle Scholar
Jasny, L., & Lubell, M. (2015). Two-mode brokerage in policy networks. Social Networks, 41, 36–47.CrossRefGoogle Scholar
Larsen, A. G., & Ellersgaard, C. H. (2017). Identifying power elites—k-cores in heterogenous affiliation networks. Social Networks, 50(1), 55–69.CrossRefGoogle Scholar
Latapy, M., Magnien, C., & Del Vecchio, N. (2008). Basic notions for the analysis of large two- mode networks. Social Networks, 30(1), 31–48.CrossRefGoogle Scholar
Newman, M. E. J. (2001). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E, 64, 016132.CrossRefGoogle ScholarPubMed
Newman, M. E. J. (2010). Networks: An introduction. Oxford: University of Oxford Publishers.CrossRefGoogle Scholar
Opsahl, T., Agneessens, F., & Skvortez, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251.CrossRefGoogle Scholar
Opsahl, T. (2013). Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. Social Networks, 35(2), 159–167.CrossRefGoogle Scholar
Urban, D. L., Minor, E. S., Treml, E. A., & Schick, R. S. (2009). Graph models of habitat mosaics. Ecology Letters, 12(3), 260–273.CrossRefGoogle ScholarPubMed
Valente, T. W. (2012). Network interventions. Science, 337(6090), 49–53.CrossRefGoogle ScholarPubMed
Wang, P., Sharpe, K., Robins, G. L., & Pattison, P. (2009). Exponential random graph (p*) models for affiliation networks. Social Networks, 31(1), 12–25.CrossRefGoogle Scholar
Wang, P., Pattison, P., & Robins, G. (2013). Exponential random graph model specifications for bipartite networks—A dependence hierarchy. Social Networks, 35(2), 211–212.CrossRefGoogle Scholar