mlsanityData to 14 Oct 2026

Disentangling homophily, community structure and triadic closure in networks

arXiv 2101.02510 ↗First posted 7 Jan 2021cs.SI physics.data-an physics.soc-phcited by 6

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 24 of its 83 references that are in the map. First posted on arXiv 7 Jan 2021.

Builds on

24 of 83 references.

References · 24

  1. 2017Cooperative Game Theory Approaches for Network Partitioningcited by 135
  2. 2020Networks beyond pairwise interactions: structure and dynamicscited by 130
  3. 2016Higher-order organization of complex networkscited by 110
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  5. 2015Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveyscited by 60
  6. 2013Hierarchical Block Structures and High-resolution Model Selection in Large Networkscited by 37
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  10. 2020Hypergraph reconstruction from network datacited by 18
  11. 2018Evaluating Overfit and Underfit in Models of Network Community Structurecited by 17
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12 more
  1. 2018Cumulative effects of triadic closure and homophily in social networkscited by 10
  2. 2020Merge-split Markov chain Monte Carlo for community detectioncited by 10
  3. 2016Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locationscited by 8
  4. 2020Revealing consensus and dissensus between network partitionscited by 7
  5. 2017Consistencies and inconsistencies between model selection and link prediction in networkscited by 6
  6. 2017Cover time for random walks on arbitrary complex networkscited by 5
  7. 2020Latent Poisson models for networks with heterogeneous densitycited by 5
  8. 2020Generative model for reciprocity and community detection in networkscited by 4
  9. 2013Organization Mining Using Online Social Networkscited by 3
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  12. 2020Atomic subgraphs and the statistical mechanics of networkscited by 2

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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 0; 2025: 2.

6 papers.

  1. 2022The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networkscited by 5
  2. 2021Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truthscited by 3
  3. 2022Implicit models, latent compression, intrinsic biases, and cheap lunches in community detectioncited by 3
  4. 2022Systematic assessment of the quality of fit of the stochastic block model for empirical networkscited by 1
  5. 2025Directed Link Prediction using GNN with Local and Global Feature Fusioncited by 0
  6. 2025Reliable data clustering with Bayesian community detectioncited by 0