Identifying multi-scale communities in networks by asymptotic surprise

TL;DR

This paper introduces a multi-resolution community detection method based on asymptotic surprise, addressing resolution limitations and improving the Louvain algorithm for revealing multi-scale structures in networks.

Contribution

It provides a theoretical analysis of asymptotic surprise's phase transition behavior and develops an improved Louvain algorithm for multi-scale community detection.

Findings

Validated the critical behaviors of asymptotic surprise

Demonstrated the improved Louvain algorithm's effectiveness

Confirmed ability to detect multi-scale communities

Abstract

Optimizing statistical measures for community structure is one of the most popular strategies for community detection, but many of them lack the flexibility of resolution and thus are incompatible with multi-scale communities of networks. Here, we further studied a statistical measure of interest for community detection, asymptotic surprise, an asymptotic approximation of surprise. We discussed the critical behaviors of asymptotic surprise in phase transition of community partition theoretically. Then, according to the theoretical analysis, a multi-resolution method based on asymptotic surprise was introduced, which provides an alternative approach to study multi-scale networks, and an improved Louvain algorithm was proposed to optimize the asymptotic surprise more effectively. By a series of experimental tests in various networks, we validated the critical behaviors of the asymptotic surprise further and the effectiveness of the improved Louvain algorithm, displayed its ability to solve the first-type resolution limit and stronger tolerance against the second-type resolution limit, and confirmed its effectiveness of revealing multi-scale community structures in multi-scale networks.