Community structure in directed networks

TL;DR

This paper introduces a method to detect communities in directed networks by generalizing modularity to include edge directions, leading to improved community detection results over previous undirected approaches.

Contribution

It presents a novel modularity-based algorithm that incorporates edge directions for community detection in directed networks, improving accuracy.

Findings

Better community detection performance on real and synthetic networks

Generalized modularity effectively captures directionality information

Algorithm outperforms previous undirected methods

Abstract

We consider the problem of finding communities or modules in directed networks. The most common approach to this problem in the previous literature has been simply to ignore edge direction and apply methods developed for community discovery in undirected networks, but this approach discards potentially useful information contained in the edge directions. Here we show how the widely used benefit function known as modularity can be generalized in a principled fashion to incorporate the information contained in edge directions. This in turn allows us to find communities by maximizing the modularity over possible divisions of a network, which we do using an algorithm based on the eigenvectors of the corresponding modularity matrix. This method is shown to give demonstrably better results than previous methods on a variety of test networks, both real and computer-generated.