Uncovering the Local Hidden Community Structure in Social Networks

TL;DR

This paper introduces a local spectral method for detecting hidden community structures in social networks by iteratively expanding and removing communities from subgraphs, outperforming existing global detection methods.

Contribution

The paper presents a novel local community detection approach that effectively uncovers hidden layers by iterative subgraph sampling and community boosting, avoiding inaccuracies of global methods.

Findings

Significantly outperforms state-of-the-art baselines

Effectively detects multiple hidden community layers

Reduces detection inaccuracies caused by broken communities

Abstract

Hidden community is a useful concept proposed recently for social network analysis. To handle the rapid growth of network scale, in this work, we explore the detection of hidden communities from the local perspective, and propose a new method that detects and boosts each layer iteratively on a subgraph sampled from the original network. We first expand the seed set from a single seed node based on our modified local spectral method and detect an initial dominant local community. Then we temporarily remove the members of this community as well as their connections to other nodes, and detect all the neighborhood communities in the remaining subgraph, including some "broken communities" that only contain a fraction of members in the original network. The local community and neighborhood communities form a dominant layer, and by reducing the edge weights inside these communities, we weaken this layer's structure to reveal the hidden layers. Eventually, we repeat the whole process and all communities containing the seed node can be detected and boosted iteratively. We theoretically show that our method can avoid some situations that a broken community and the local community are regarded as one community in the subgraph, leading to the inaccuracy on detection which can be caused by global hidden community detection methods. Extensive experiments show that our method could significantly outperform the state-of-the-art baselines designed for either global hidden community detection or multiple local community detection.