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A Review of Stochastic Block Models and Extensions for Graph Clustering

Lookup NU author(s): Dr Clement LeeORCiD, Professor Darren Wilkinson

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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.

Publication metadata

Author(s): Lee C, Wilkinson DJ

Publication type: Article

Publication status: Published

Journal: Applied Network Science

Year: 2019

Volume: 4

Online publication date: 23/12/2019

Acceptance date: 08/11/2019

Date deposited: 05/01/2020

ISSN (electronic): 2364-8228

Publisher: Springer Open

URL: https://doi.org/10.1007/s41109-019-0232-2

DOI: 10.1007/s41109-019-0232-2

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