A seed-edge-based link clustering LPA for robust overlapping community detection

Abstract

In recent years, overlapping community detection in complex network has become a vital step to understand the structure of networks in various fields. At present, node-based label propagation algorithms are widely used in overlapping community detection research because of its simple and rapid advantages. However, these kinds of algorithms are always random, and sometimes even divide all nodes into one community. In this paper, to detect overlapping communities in complex networks and improve the robustness of label propagation algorithms, edges are chosen for detection instead of nodes, because overlapping community structures can be naturally obtained by edge-based detection algorithms. We propose a seed-edge-based link clustering label propagation algorithm (SELPA). Meanwhile, to improve the accuracy of the algorithm, the SELPA algorithm merges and optimizes the overlapping communities from the perspective of improving the modularity. Experiments on several real-world networks demonstrate that the proposed method is more robust and accurate than the existing algorithms based on label propagation in overlapping community detection.