2015/06/30 by Federico Battiston, Jacopo Iacovacci, Vincenzo Nicosia +2 · 80 citations
Computer Science · Physics and Astronomy · Social Sciences · #Assortativity #Bioinformatics #Biology #Business #Closure (psychology) #Community structure #Complex Network Analysis Techniques #Complex network #Computer science #Computer security #Data science #Ecology #Evolutionary Game Theory and Cooperation #Exploit #Layer (electronics) #Multiplex #Nanotechnology #Opinion Dynamics and Social Influence #Order (exchange) #Political science #World Wide Web #cs.SI #physics.soc-ph
paper · pdf · doi:10.1371/journal.pone.0147451
published in PLoS ONE 11(1), e0147451 (Public Library of Science) · 11 pages, 6 figures, published in Plos One
openalex publication_date 2016/01/27 · arxiv created 2016/01/28 · arxiv updated 2016/01/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Community structures in collaboration networks reflect the natural tendency of individuals to organize their work in groups in order to better achieve common goals. In most of the cases, individuals exploit their connections to introduce themselves to new areas of interests, giving rise to multifaceted collaborations which span different fields. In this paper, we analyse collaborations in science and among movie actors as multiplex networks, where the layers represent respectively research topics and movie genres, and we show that communities indeed coexist and overlap at the different layers of such systems. We then propose a model to grow multiplex networks based on two mechanisms of intra and inter-layer triadic closure which mimic the real processes by which collaborations evolve. We show that our model is able to explain the multiplex community structure observed empirically, and we infer the strength of the two underlying social mechanisms from real-world systems. Being also able to correctly reproduce the values of intra-layer and inter-layer assortativity correlations, the model contributes to a better understanding of the principles driving the evolution of social networks.