# A Review of Stochastic Block Models and Extensions for Graph Clustering

## TL;DR

This paper provides a comprehensive review of stochastic block models and their extensions for graph clustering, covering various approaches, inference methods, and applications to different data types.

## Contribution

It offers a concise overview of recent developments and compares different models and methods in the field of graph clustering using stochastic block models.

## Key findings

- Summarizes various stochastic block model approaches and extensions.
- Compares methods for different graph types and data integration.
- Highlights challenges and future directions in the field.

## Abstract

There have been rapid developments in model-based clustering of graphs, also known as block modelling, over the last ten years or so. We review different approaches and extensions proposed for different aspects in this area, such as the type of the graph, the clustering approach, the inference approach, and whether the number of groups is selected or estimated. We also review models that combine block modelling with topic modelling and/or longitudinal modelling, regarding how these models deal with multiple types of data. How different approaches cope with various issues will be summarised and compared, to facilitate the demand of practitioners for a concise overview of the current status of these areas of literature.