Improved Community Detection using Stochastic Block Models

TL;DR

This paper evaluates the connectivity of communities detected by Stochastic Block Models and introduces a simple post-processing technique, Well-Connected Clusters, to improve their internal connectivity and clustering accuracy.

Contribution

The paper identifies the issue of disconnected communities in SBMs and proposes WCC as an effective, scalable post-processing method to enhance community connectivity and detection accuracy.

Findings

WCC improves the connectivity of communities detected by SBMs.

Using WCC as a post-processing step enhances clustering accuracy.

WCC is efficient and scalable for large networks.

Abstract

Identifying edge-dense communities that are also well-connected is an important aspect of understanding community structure. Prior work has shown that community detection methods can produce poorly connected communities, and some can even produce internally disconnected communities. In this study we evaluate the connectivity of communities obtained using Stochastic Block Models. We find that SBMs produce internally disconnected communities from real-world networks. We present a simple technique, Well-Connected Clusters (WCC), which repeatedly removes small edge cuts until the communities meet a user-specified threshold for well-connectivity. Our study using a large collection of synthetic networks based on clustered real-world networks shows that using WCC as a post-processing tool with SBM community detection typically improves clustering accuracy. WCC is fast enough to use on networks with millions of nodes and is freely available in open source form.