Community detection and graph partitioning

TL;DR

This paper demonstrates that popular statistical inference methods for community detection can be reformulated as graph partitioning problems, enabling the use of efficient algorithms and improving performance on various networks.

Contribution

It shows how two widely used inference methods can be mapped onto minimum-cut graph partitioning, facilitating better algorithms for community detection.

Findings

Spectral partitioning adapted for community inference performs well

The approach yields results comparable to the best existing methods

The method is efficient on both synthetic and real-world networks

Abstract

Many methods have been proposed for community detection in networks. Some of the most promising are methods based on statistical inference, which rest on solid mathematical foundations and return excellent results in practice. In this paper we show that two of the most widely used inference methods can be mapped directly onto versions of the standard minimum-cut graph partitioning problem, which allows us to apply any of the many well-understood partitioning algorithms to the solution of community detection problems. We illustrate the approach by adapting the Laplacian spectral partitioning method to perform community inference, testing the resulting algorithm on a range of examples, including computer-generated and real-world networks. Both the quality of the results and the running time rival the best previous methods.