Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion

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.

Detection of Community Structures in Networks With Nodal Features based on Generative Probabilistic Approach

“…W which captures the correlation level between the communities and the generative features, is updated by FindDerivationW and UpdateW according to equations (15) and (16). β is updated based on equations (17) and (18). The update procedure is repeated until the convergence criterion is met.…”

Hybrid Optimization Algorithm for Community and Fraud Detection in Complex Networks for High Immunity Towards Link and Node Failures

“…Whang et al [20] have introduced an algorithm using seed expansion approach for detecting efficient overlapping community. The algorithm is based on community metrics to find and expand the good seed nodes.…”

Detecting semantic‐based communities in node‐attributed graphs

“…As is known to all, finding out overlapping communities is meaningful according to real situations. In addition, seed-centric approaches 19,20 have also gained considerable interest in which the selected seeds are expanded or merged to obtain overlapping results. 16,17 Recently, edge clustering 18 has gained much attention, which is a novel method for revealing overlapping communities.…”