Fast unfolding of communities in large networks

TL;DR

This paper introduces a fast heuristic algorithm for detecting community structures in large networks, significantly improving computation time while maintaining high modularity quality, demonstrated on large-scale real-world and synthetic networks.

Contribution

The paper presents a novel, efficient heuristic for community detection based on modularity optimization that outperforms existing methods in speed and quality.

Findings

Outperforms existing methods in computation time

Detects meaningful communities in large-scale networks

Validated on real-world and synthetic networks

Abstract

We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks. .