Identification of hybrid node and link communities in complex networks

TL;DR

This paper introduces a novel probabilistic approach for identifying hybrid node-link communities in complex networks, effectively capturing intricate structures and outperforming existing methods in real-world applications.

Contribution

The paper presents a new hybrid community detection scheme combining node and link communities with a probabilistic model, improving analysis of complex network structures.

Findings

Hybrid communities reveal network characteristics more effectively.

The approach outperforms existing node or link community detection methods.

Experiments on real-world networks demonstrate superior performance.

Abstract

Identification of communities in complex networks has become an effective means to analysis of complex systems. It has broad applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network structure analysis. These schemes, however, have inherent drawbacks and are often inadequate to properly capture complex organizational structures in real networks. We introduce a new scheme and effective approach for identifying complex network structures using a mixture of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large semantic association network of commonly used words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.