Group detection in complex networks: An algorithm and comparison of the state of the art

TL;DR

This paper introduces a simple, efficient propagation-based algorithm for detecting various types of groups in complex networks, outperforming existing methods in general group detection and link prediction tasks.

Contribution

A novel hierarchical group detection algorithm that requires no prior knowledge and effectively identifies different group types in complex networks.

Findings

Comparable to state-of-the-art in community detection

Superior in general group detection

Better in link prediction tasks

Abstract

Complex real-world networks commonly reveal characteristic groups of nodes like communities and modules. These are of value in various applications, especially in the case of large social and information networks. However, while numerous community detection techniques have been presented in the literature, approaches for other groups of nodes are relatively rare and often limited in some way. We present a simple propagation-based algorithm for general group detection that requires no a priori knowledge and has near ideal complexity. The main novelty here is that different types of groups are revealed through an adequate hierarchical group refinement procedure. The proposed algorithm is validated on various synthetic and real-world networks, and rigorously compared against twelve other state-of-the-art approaches on group detection, hierarchy discovery and link prediction tasks. The algorithm is comparable to the state of the art in community detection, while superior in general group detection and link prediction. Based on the comparison, we also dis- cuss some prominent directions for future work on group detection in complex networks.