Community detection through vector-label propagation algorithms

Community detection is a fundamental and important problem in network science, as community structures often reveal both topological and functional relationships between different components of the complex system. In this paper, we first propose a gradient descent framework of modularity optimization called vector-label propagation algorithm (VLPA), where a node is associated with a vector of continuous community labels instead of one label. Retaining weak structural information in vector-label, VLPA outperforms some well-known community detection methods, and particularly improves the performance in networks with weak community structures. Further, we incorporate stochastic gradient strategies into VLPA to avoid stuck in the local optima, leading to the stochastic vector-label propagation algorithm (sVLPA). We show that sVLPA performs better than Louvain Method, a widely used community detection algorithm, on both artificial benchmarks and realworld networks. Our theoretical scheme based on vector-label propagation can be directly applied to highdimensional networkswhere each node hasmultiple features, and can also be used for optimizing other partition measures such as modularity with resolution parameters. (C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a gradient descent framework called vector-label propagation algorithm (VLPA) for modularity optimization in community detection. By retaining weak structural information in vector-label, VLPA outperforms some well-known community detection methods, especially in networks with weak community structures. The authors further incorporate stochastic gradient strategies into VLPA and develop the stochastic vector-label propagation algorithm (sVLPA), which performs better than the widely used Louvain Method on artificial benchmarks and real-world networks.