# Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction

arXiv: https://arxiv.org/abs/1501.06218 · HTML: https://mlsanity.com/paper/1501.06218/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2015.

## Built on by (most cited first, 23 of 23)

- [ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting](https://mlsanity.com/paper/2205.13700/) (2022)
- [Advancing Graph Generation through Beta Diffusion](https://mlsanity.com/paper/2406.09357/) (2024)
- [A Variational Edge Partition Model for Supervised Graph Representation Learning](https://mlsanity.com/paper/2202.03233/) (2022)
- [Bayesian community detection for networks with covariates](https://mlsanity.com/paper/2203.02090/) (2022)
- [Zero-Truncated Poisson Regression for Sparse Multiway Count Data Corrupted by False Zeros](https://mlsanity.com/paper/2201.10014/) (2022)
- [EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model](https://mlsanity.com/paper/2208.06322/) (2022)
- [A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion](https://mlsanity.com/paper/2304.13940/) (2023)
- [A majorization-minimization algorithm for nonnegative binary matrix factorization](https://mlsanity.com/paper/2204.09741/) (2022)
- [Nested stochastic block model for simultaneously clustering networks and nodes](https://mlsanity.com/paper/2307.09210/) (2023)
- [Bayesian Vector AutoRegression with Factorised Granger-Causal Graphs](https://mlsanity.com/paper/2402.03614/) (2024)
- [Negative-Binomial Randomized Gamma Markov Processes for Heterogeneous Overdispersed Count Time Series](https://mlsanity.com/paper/2402.18995/) (2024)
- [Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics](https://mlsanity.com/paper/2406.03242/) (2024)
- [Disentangled Generative Graph Representation Learning](https://mlsanity.com/paper/2408.13471/) (2024)
- [Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks](https://mlsanity.com/paper/2410.09696/) (2024)
- [Poisson-Gamma Modeling of Inter-Relational Dependencies in Dynamic Knowledge Graphs](https://mlsanity.com/paper/2607.02872/) (2026)
- [A Survey on Machine Learning Solutions for Graph Pattern Extraction](https://mlsanity.com/paper/2204.01057/) (2022)
- [Ordinal Graph Gamma Belief Network for Social Recommender Systems](https://mlsanity.com/paper/2209.05106/) (2022)
- [Overlapping Community Detection using Dynamic Dilated Aggregation in Deep Residual GCN](https://mlsanity.com/paper/2210.11174/) (2022)
- [A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty](https://mlsanity.com/paper/2304.04497/) (2023)
- [Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC](https://mlsanity.com/paper/2403.00044/) (2024)
- [Hierarchical Blockmodelling for Knowledge Graphs](https://mlsanity.com/paper/2408.15649/) (2024)
- [A Local Perspective-based Model for Overlapping Community Detection](https://mlsanity.com/paper/2503.21558/) (2025)
- [Bayesian Poisson-Randomized Gamma Tensor Factorization with Application to International Trade Flows](https://mlsanity.com/paper/2606.17267/) (2026)

## Abstract (arXiv)

A hierarchical gamma process infinite edge partition model is proposed to factorize the binary adjacency matrix of an unweighted undirected relational network under a Bernoulli-Poisson link. The model describes both homophily and stochastic equivalence, and is scalable to big sparse networks by focusing its computation on pairs of linked nodes. It can not only discover overlapping communities and inter-community interactions, but also predict missing edges. A simplified version omitting inter-community interactions is also provided and we reveal its interesting connections to existing models. The number of communities is automatically inferred in a nonparametric Bayesian manner, and efficient inference via Gibbs sampling is derived using novel data augmentation techniques. Experimental results on four real networks demonstrate the models' scalability and state-of-the-art performance.