# Efficiently inferring community structure in bipartite networks

arXiv: https://arxiv.org/abs/1403.2933 · HTML: https://mlsanity.com/paper/1403.2933/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2014.

## Built on by (most cited first, 7 of 7)

- [Generative hypergraph clustering: from blockmodels to modularity](https://mlsanity.com/paper/2101.09611/) (2021)
- [Efficient High-Quality Clustering for Large Bipartite Graphs](https://mlsanity.com/paper/2312.16926/) (2023)
- [Hypergraph Artificial Benchmark for Community Detection (h-ABCD)](https://mlsanity.com/paper/2210.15009/) (2022)
- [Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truths](https://mlsanity.com/paper/2112.00183/) (2021)
- [Choice Set Confounding in Discrete Choice](https://mlsanity.com/paper/2105.07959/) (2021)
- [Matrix Factorization Framework for Community Detection under the Degree-Corrected Block Model](https://mlsanity.com/paper/2601.06262/) (2026)
- [Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems](https://mlsanity.com/paper/2604.18351/) (2026)

## Abstract (arXiv)

Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite networks by formulating a bipartite stochastic block model, which explicitly includes vertex type information and may be trivially extended to k-partite networks. This bipartite stochastic block model yields a projection-free and statistically principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results. We demonstrate this model's ability to efficiently and accurately find community structure in synthetic bipartite networks with known structure and in real-world bipartite networks with unknown structure, and we characterize its performance in practical contexts.