Fast Multi-Scale Detection of Relevant Communities

TL;DR

This paper introduces a fast, multi-scale community detection method applicable to various network criteria, significantly reducing computational complexity and enabling efficient analysis of large, multi-scale networks.

Contribution

The paper presents two algorithms for multi-scale community detection compatible with multiple criteria, with heuristics that improve speed and accuracy on large networks.

Findings

Effective multi-scale detection across six criteria

Significant reduction in computational complexity

High accuracy demonstrated on large networks

Abstract

Nowadays, networks are almost ubiquitous. In the past decade, community detection received an increasing interest as a way to uncover the structure of networks by grouping nodes into communities more densely connected internally than externally. Yet most of the effective methods available do not consider the potential levels of organisation, or scales, a network may encompass and are therefore limited. In this paper we present a method compatible with global and local criteria that enables fast multi-scale community detection. The method is derived in two algorithms, one for each type of criterion, and implemented with 6 known criteria. Uncovering communities at various scales is a computationally expensive task. Therefore this work puts a strong emphasis on the reduction of computational complexity. Some heuristics are introduced for speed-up purposes. Experiments demonstrate the efficiency and accuracy of our method with respect to each algorithm and criterion by testing them against large generated multi-scale networks. This study also offers a comparison between criteria and between the global and local approaches.