# vGraph: A Generative Model for Joint Community Detection and Node Representation Learning

arXiv: https://arxiv.org/abs/1906.07159 · HTML: https://mlsanity.com/paper/1906.07159/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2019.

## Built on by (most cited first, 26 of 26)

- [A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2101.01669/) (2021)
- [Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction](https://mlsanity.com/paper/2202.00961/) (2022)
- [Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding](https://mlsanity.com/paper/2110.07582/) (2021)
- [Trans4E: Link Prediction on Scholarly Knowledge Graphs](https://mlsanity.com/paper/2107.03297/) (2021)
- [A Comprehensive Review of Community Detection in Graphs](https://mlsanity.com/paper/2309.11798/) (2023)
- [Decoupled Self-supervised Learning for Non-Homophilous Graphs](https://mlsanity.com/paper/2206.03601/) (2022)
- [Calibrating and Improving Graph Contrastive Learning](https://mlsanity.com/paper/2101.11525/) (2021)
- [A Variational Edge Partition Model for Supervised Graph Representation Learning](https://mlsanity.com/paper/2202.03233/) (2022)
- [CIMAGE: Exploiting the Conditional Independence in Masked Graph Auto-encoders](https://mlsanity.com/paper/2503.07852/) (2025)
- [Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings](https://mlsanity.com/paper/2111.03030/) (2021)
- [Deep Graph Clustering via Mutual Information Maximization and Mixture Model](https://mlsanity.com/paper/2205.05168/) (2022)
- [META-CODE: Community Detection via Exploratory Learning in Topologically Unknown Networks](https://mlsanity.com/paper/2208.11015/) (2022)
- [Aitchison Embeddings for Learning Compositional Graph Representations](https://mlsanity.com/paper/2605.00716/) (2026)
- [GlobalWalk: Learning Global-aware Node Embeddings via Biased Sampling](https://mlsanity.com/paper/2201.09882/) (2022)
- [New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction](https://mlsanity.com/paper/2211.08972/) (2022)
- [DBGDGM: Dynamic Brain Graph Deep Generative Model](https://mlsanity.com/paper/2301.11408/) (2023)
- [PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities](https://mlsanity.com/paper/2409.11618/) (2024)
- [Variational Embeddings for Community Detection and Node Representation](https://mlsanity.com/paper/2101.03885/) (2021)
- [Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach](https://mlsanity.com/paper/2201.09086/) (2022)
- [Can one hear the position of nodes?](https://mlsanity.com/paper/2211.06325/) (2022)
- [Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering](https://mlsanity.com/paper/2303.12212/) (2023)
- [A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty](https://mlsanity.com/paper/2304.04497/) (2023)
- [Changepoint Detection in Highly-Attributed Dynamic Graphs](https://mlsanity.com/paper/2407.06998/) (2024)
- [New Recipe for Semi-supervised Community Detection: Clique Annealing under Crystallization Kinetics](https://mlsanity.com/paper/2504.15927/) (2025)
- [Advancing Community Detection with Graph Convolutional Neural Networks: Bridging Topological and Attributive Cohesion](https://mlsanity.com/paper/2505.10197/) (2025)
- [ATLAS: Adaptive Topology-based Learning at Scale for Homophilic and Heterophilic Graphs](https://mlsanity.com/paper/2512.14908/) (2025)

## Abstract (arXiv)

This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs, respectively. In the current literature, these two tasks are usually independently studied while they are actually highly correlated. We propose a probabilistic generative model called vGraph to learn community membership and node representation collaboratively. Specifically, we assume that each node can be represented as a mixture of communities, and each community is defined as a multinomial distribution over nodes. Both the mixing coefficients and the community distribution are parameterized by the low-dimensional representations of the nodes and communities. We designed an effective variational inference algorithm which regularizes the community membership of neighboring nodes to be similar in the latent space. Experimental results on multiple real-world graphs show that vGraph is very effective in both community detection and node representation learning, outperforming many competitive baselines in both tasks. We show that the framework of vGraph is quite flexible and can be easily extended to detect hierarchical communities.