Fast algorithm for detecting community structure in networks

TL;DR

This paper introduces a new, significantly faster algorithm for detecting community structures in networks, capable of handling large-scale real-world networks efficiently.

Contribution

The paper presents a novel community detection algorithm that is thousands of times faster than existing methods, enabling analysis of large networks.

Findings

Excellent performance on both synthetic and real-world networks

Able to analyze networks with over 50,000 nodes efficiently

Demonstrated application on a large physicist collaboration network

Abstract

It has been found that many networks display community structure -- groups of vertices within which connections are dense but between which they are sparser -- and highly sensitive computer algorithms have in recent years been developed for detecting such structure. These algorithms however are computationally demanding, which limits their application to small networks. Here we describe a new algorithm which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster than previous algorithms. We give several example applications, including one to a collaboration network of more than 50000 physicists.