# Disentangling homophily, community structure and triadic closure in networks

arXiv: https://arxiv.org/abs/2101.02510 · HTML: https://mlsanity.com/paper/2101.02510/

## 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 (most cited first, 24 of 24)

- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)
- [Networks beyond pairwise interactions: structure and dynamics](https://mlsanity.com/paper/2006.01764/) (2020)
- [Higher-order organization of complex networks](https://mlsanity.com/paper/1612.08447/) (2016)
- [Simplicial Closure and higher-order link prediction](https://mlsanity.com/paper/1802.06916/) (2018)
- [Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys](https://mlsanity.com/paper/1506.03645/) (2015)
- [Hierarchical Block Structures and High-resolution Model Selection in Large Networks](https://mlsanity.com/paper/1310.4377/) (2013)
- [Stacking Models for Nearly Optimal Link Prediction in Complex Networks](https://mlsanity.com/paper/1909.07578/) (2019)
- [Bayesian stochastic blockmodeling](https://mlsanity.com/paper/1705.10225/) (2017)
- [Nonparametric Bayesian inference of the microcanonical stochastic block model](https://mlsanity.com/paper/1610.02703/) (2016)
- [Hypergraph reconstruction from network data](https://mlsanity.com/paper/2008.04948/) (2020)
- [Evaluating Overfit and Underfit in Models of Network Community Structure](https://mlsanity.com/paper/1802.10582/) (2018)
- [Reconstructing networks with unknown and heterogeneous errors](https://mlsanity.com/paper/1806.07956/) (2018)
- [Cumulative effects of triadic closure and homophily in social networks](https://mlsanity.com/paper/1809.06057/) (2018)
- [Merge-split Markov chain Monte Carlo for community detection](https://mlsanity.com/paper/2003.07070/) (2020)
- [Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations](https://mlsanity.com/paper/1607.06565/) (2016)
- [Revealing consensus and dissensus between network partitions](https://mlsanity.com/paper/2005.13977/) (2020)
- [Consistencies and inconsistencies between model selection and link prediction in networks](https://mlsanity.com/paper/1705.07967/) (2017)
- [Cover time for random walks on arbitrary complex networks](https://mlsanity.com/paper/1706.02356/) (2017)
- [Latent Poisson models for networks with heterogeneous density](https://mlsanity.com/paper/2002.07803/) (2020)
- [Generative model for reciprocity and community detection in networks](https://mlsanity.com/paper/2012.08215/) (2020)
- [Organization Mining Using Online Social Networks](https://mlsanity.com/paper/1303.3741/) (2013)
- [Identifying modular flows on multilayer networks reveals highly overlapping organization in social systems](https://mlsanity.com/paper/1408.2925/) (2014)
- [Sampling motif-constrained ensembles of networks](https://mlsanity.com/paper/1507.08696/) (2015)
- [Atomic subgraphs and the statistical mechanics of networks](https://mlsanity.com/paper/2008.10346/) (2020)

## Built on by (most cited first, 6 of 6)

- [The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks](https://mlsanity.com/paper/2205.00639/) (2022)
- [Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truths](https://mlsanity.com/paper/2112.00183/) (2021)
- [Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection](https://mlsanity.com/paper/2210.09186/) (2022)
- [Systematic assessment of the quality of fit of the stochastic block model for empirical networks](https://mlsanity.com/paper/2201.01658/) (2022)
- [Directed Link Prediction using GNN with Local and Global Feature Fusion](https://mlsanity.com/paper/2506.20235/) (2025)
- [Reliable data clustering with Bayesian community detection](https://mlsanity.com/paper/2510.15013/) (2025)

## 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.