Disentangling homophily, community structure and triadic closure in networks

Abstract

Network homophily, the tendency of similar nodes to be connected, and transitivity, the tendency of two nodes being connected if they share a common neighbor, are conflated properties in network analysis, since one mechanism can drive the other. Here we present a generative model and corresponding inference procedure that are capable of distinguishing between both mechanisms. Our approach is based on a variation of the stochastic block model (SBM) with the addition of triadic closure edges, and its inference can identify the most plausible mechanism responsible for the existence of every edge in the network, in addition to the underlying community structure itself. We show how the method can evade the detection of spurious communities caused solely by the formation of triangles in the network, and how it can improve the performance of edge prediction when compared to the pure version of the SBM without triadic closure.

23 pages, 10 figures

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Cited by in corpus (10)

• The physics of higher-order interactions in complex systems
• Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truths
• Assortative and preferential attachment lead to core-periphery networks
• Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection
• Random graphs and real networks with weak geometric coupling
• Entropy of microcanonical finite-graph ensembles
• Strongly clustered random graphs via triadic closure: An exactly solvable model
• The latent cognitive structures of social networks
• Systematic assessment of the quality of fit of the stochastic block model for empirical networks
• Emergence of network communities driven by local rules