Community Detection through Vector-label Propagation Algorithms

TL;DR

This paper introduces a novel vector-label propagation algorithm for community detection that enhances performance, especially in networks with weak community structures, by using continuous labels and stochastic strategies.

Contribution

It proposes a gradient descent framework with vector labels for modularity optimization and incorporates stochastic strategies to improve community detection accuracy.

Findings

VLPA outperforms some existing methods in weak community networks

sVLPA surpasses Louvain Method in benchmarks and real networks

The approach applies to high-dimensional networks with multiple node features

Abstract

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 real-world networks. Our theoretical scheme based on vector-label propagation can be directly applied to high-dimensional networks where each node has multiple features, and can also be used for optimizing other partition measures such as modularity with resolution parameters.