Multi-scale Community Detection using Stability Optimisation within Greedy Algorithms

TL;DR

This paper introduces a stability-based optimization method for multi-scale community detection in networks, utilizing Markov processes, with heuristics and applications to overlapping communities, demonstrating accurate analysis.

Contribution

It presents a novel stability optimization approach for multi-scale community detection, including heuristics and methods for overlapping communities.

Findings

Enables accurate multi-scale network analysis

Effective for detecting overlapping communities

Outperforms some existing methods in experiments

Abstract

Many real systems can be represented as networks whose analysis can be very informative regarding the original system's organisation. In the past decade community detection received a lot of attention and is now an active field of research. Recently stability was introduced as a new measure for partition quality. This work investigates stability as an optimisation criterion that exploits a Markov process view of networks to enable multi-scale community detection. Several heuristics and variations of an algorithm optimising stability are presented as well as an application to overlapping communities. Experiments show that the method enables accurate multi-scale network analysis.