# Stochastic Blockmodels meet Graph Neural Networks

arXiv: https://arxiv.org/abs/1905.05738 · HTML: https://mlsanity.com/paper/1905.05738/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 30 of 39)

- [Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling](https://mlsanity.com/paper/2305.04111/) (2023)
- [Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization](https://mlsanity.com/paper/2102.06966/) (2021)
- [Learning from Counterfactual Links for Link Prediction](https://mlsanity.com/paper/2106.02172/) (2021)
- [Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2204.00570/) (2022)
- [A Comprehensive Survey on Community Detection with Deep Learning](https://mlsanity.com/paper/2105.12584/) (2021)
- [A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2101.01669/) (2021)
- [Effects of Graph Convolutions in Multi-layer Networks](https://mlsanity.com/paper/2204.09297/) (2022)
- [Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective](https://mlsanity.com/paper/2207.11311/) (2022)
- [A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks](https://mlsanity.com/paper/2307.01951/) (2023)
- [NVDiff: Graph Generation through the Diffusion of Node Vectors](https://mlsanity.com/paper/2211.10794/) (2022)
- [Advancing Graph Generation through Beta Diffusion](https://mlsanity.com/paper/2406.09357/) (2024)
- [Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons](https://mlsanity.com/paper/2306.13926/) (2023)
- [Learning Ideological Embeddings from Information Cascades](https://mlsanity.com/paper/2109.13589/) (2021)
- [A Variational Edge Partition Model for Supervised Graph Representation Learning](https://mlsanity.com/paper/2202.03233/) (2022)
- [Node Copying: A Random Graph Model for Effective Graph Sampling](https://mlsanity.com/paper/2208.02435/) (2022)
- [Interpretable Node Representation with Attribute Decoding](https://mlsanity.com/paper/2212.01682/) (2022)
- [EDGE++: Improved Training and Sampling of EDGE](https://mlsanity.com/paper/2310.14441/) (2023)
- [Hierarchical Infinite Relational Model](https://mlsanity.com/paper/2108.07208/) (2021)
- [Analysis of Corrected Graph Convolutions](https://mlsanity.com/paper/2405.13987/) (2024)
- [BN-Pool: Bayesian Nonparametric Pooling for Graphs](https://mlsanity.com/paper/2501.09821/) (2025)
- [Barlow Graph Auto-Encoder for Unsupervised Network Embedding](https://mlsanity.com/paper/2110.15742/) (2021)
- [DBGDGM: Dynamic Brain Graph Deep Generative Model](https://mlsanity.com/paper/2301.11408/) (2023)
- [The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges](https://mlsanity.com/paper/2304.08242/) (2023)
- [Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification](https://mlsanity.com/paper/2403.17500/) (2024)
- [PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities](https://mlsanity.com/paper/2409.11618/) (2024)
- [Disentangled and Self-Explainable Node Representation Learning](https://mlsanity.com/paper/2410.21043/) (2024)
- [A Hidden Challenge of Link Prediction: Which Pairs to Check?](https://mlsanity.com/paper/2102.07878/) (2021)
- [Modeling Graph Node Correlations with Neighbor Mixture Models](https://mlsanity.com/paper/2103.15966/) (2021)
- [Low-Rank Projections of GCNs Laplacian](https://mlsanity.com/paper/2106.07360/) (2021)
- [A Deep Latent Space Model for Graph Representation Learning](https://mlsanity.com/paper/2106.11721/) (2021)

## Abstract (arXiv)

Stochastic blockmodels (SBM) and their variants, e.g., mixed-membership and overlapping stochastic blockmodels, are latent variable based generative models for graphs. They have proven to be successful for various tasks, such as discovering the community structure and link prediction on graph-structured data. Recently, graph neural networks, e.g., graph convolutional networks, have also emerged as a promising approach to learn powerful representations (embeddings) for the nodes in the graph, by exploiting graph properties such as locality and invariance. In this work, we unify these two directions by developing a sparse variational autoencoder for graphs, that retains the interpretability of SBMs, while also enjoying the excellent predictive performance of graph neural nets. Moreover, our framework is accompanied by a fast recognition model that enables fast inference of the node embeddings (which are of independent interest for inference in SBM and its variants). Although we develop this framework for a particular type of SBM, namely the overlapping stochastic blockmodel, the proposed framework can be adapted readily for other types of SBMs. Experimental results on several benchmarks demonstrate encouraging results on link prediction while learning an interpretable latent structure that can be used for community discovery.