Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion

TL;DR

This paper introduces an efficient overlapping community detection algorithm that uses seed expansion with novel seeding strategies, outperforming existing methods in identifying cohesive and ground-truth communities in real-world networks.

Contribution

The paper proposes a new seed expansion algorithm with innovative seeding strategies based on personalized PageRank, improving overlapping community detection accuracy.

Findings

Outperforms state-of-the-art methods in community cohesion

New seeding strategies enhance detection of ground-truth communities

Effective in real-world network applications

Abstract

Community detection is an important task in network analysis. A community (also referred to as a cluster) is a set of cohesive vertices that have more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. In this paper, we propose an efficient overlapping community detection algorithm using a seed expansion approach. The key idea of our algorithm is to find good seeds, and then greedily expand these seeds based on a community metric. Within this seed expansion method, we investigate the problem of how to determine good seed nodes in a graph. In particular, we develop new seeding strategies for a personalized PageRank clustering scheme that optimizes the conductance community score. Experimental results show that our seed expansion algorithm outperforms other state-of-the-art overlapping community detection methods in terms of producing cohesive clusters and identifying ground-truth communities. We also show that our new seeding strategies are better than existing strategies, and are thus effective in finding good overlapping communities in real-world networks.