Discovering link communities in complex networks by exploiting link dynamics

TL;DR

This paper introduces UELC, a novel link dynamics-based algorithm that employs a link-node-link random walk to identify link communities in complex networks, demonstrating high effectiveness on synthetic and real-world data.

Contribution

The paper presents a new link community detection method using link dynamics and random walks, extending it to node communities, which improves community identification accuracy.

Findings

Effective in synthetic benchmark tests

Demonstrates utility on real-world networks

Extends to node community detection

Abstract

Discovery of communities in complex networks is a fundamental data analysis problem with applications in various domains. Most of the existing approaches have focused on discovering communities of nodes, while recent studies have shown great advantages and utilities of the knowledge of communities of links in networks. From this new perspective, we propose a link dynamics based algorithm, called UELC, for identifying link communities of networks. In UELC, the stochastic process of a link-node-link random walk is employed to unfold an embedded bipartition structure of links in a network. The local mixing properties of the Markov chain underlying the random walk are then utilized to extract two emerged link communities. Further, the random walk and the bipartitioning processes are wrapped in an iterative subdivision strategy to recursively identify link partitions that segregate the network links into multiple subdivisions. We evaluate the performance of the new method on synthetic benchmarks and demonstrate its utility on real-world networks. Our experimental results show that our method is highly effective for discovering link communities in complex networks. As a comparison, we also extend UELC to extracting communities of node, and show that it is effective for node community identification.