A framework for community detection in heterogeneous multi-relational networks

TL;DR

This paper introduces a new parameter-free, scalable method for detecting communities in heterogeneous multi-relational networks by optimizing a novel composite modularity, outperforming existing techniques in synthetic and real-world networks.

Contribution

It proposes a novel composite modularity measure and a scalable, parameter-free community detection method tailored for heterogeneous multi-relational networks.

Findings

Outperforms state-of-the-art techniques in synthetic networks.

Successfully detects meaningful communities in a real-world Digg network.

Scalable and suitable for various complex network structures.

Abstract

There has been a surge of interest in community detection in homogeneous single-relational networks which contain only one type of nodes and edges. However, many real-world systems are naturally described as heterogeneous multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose a new method for detecting communities in such networks. Our method is based on optimizing the composite modularity, which is a new modularity proposed for evaluating partitions of a heterogeneous multi-relational network into communities. Our method is parameter-free, scalable, and suitable for various networks with general structure. We demonstrate that it outperforms the state-of-the-art techniques in detecting pre-planted communities in synthetic networks. Applied to a real-world Digg network, it successfully detects meaningful communities.