Towards Linear Time Overlapping Community Detection in Social Networks

TL;DR

This paper introduces SLPA, a fast, linear-time algorithm for detecting overlapping communities in large social networks, capable of handling unipartite and bipartite graphs and uncovering hierarchical structures.

Contribution

The paper presents SLPA, a novel label propagation algorithm that efficiently detects overlapping communities with linear scalability and hierarchical capabilities.

Findings

SLPA achieves high accuracy in synthetic and real-world networks.

SLPA scales linearly with the number of edges.

SLPA effectively uncovers overlapping and hierarchical community structures.

Abstract

Membership diversity is a characteristic aspect of social networks in which a person may belong to more than one social group. For this reason, discovering overlapping structures is necessary for realistic social analysis. In this paper, we present a fast algorithm1, called SLPA, for overlapping community detection in large-scale networks. SLPA spreads labels according to dynamic interaction rules. It can be applied to both unipartite and bipartite networks. It is also able to uncover overlapping nested hierarchy. The time complexity of SLPA scales linearly with the number of edges in the network. Experiments in both synthetic and real- world networks show that SLPA has an excellent performance in identifying both node and community level overlapping structures.