Community detection by label propagation with compression of flow

TL;DR

This paper introduces a modified label propagation algorithm, LPAf, that improves community detection in large networks by considering random walk compression and employing strategies to escape local optima and accelerate convergence.

Contribution

The paper presents LPAf, a novel community detection algorithm that enhances stability and efficiency over traditional label propagation methods through innovative update strategies.

Findings

LPAf outperforms basic LPA in accuracy on synthetic and real networks.

LPAf converges faster due to incomplete update conditions.

LPAf effectively detects communities in large complex networks.

Abstract

The label propagation algorithm (LPA) has been proved to be a fast and effective method for detecting communities in large complex networks. However, its performance is subject to the non-stable and trivial solutions of the problem. In this paper, we propose a modified label propagation algorithm LPAf to efficiently detect community structures in networks. Instead of the majority voting rule of the basic LPA, LPAf updates the label of a node by considering the compression of a description of random walks on a network. A multi-step greedy agglomerative strategy is employed to enable LPAf to escape the local optimum. Furthermore, an incomplete update condition is also adopted to speed up the convergence. Experimental results on both synthetic and real-world networks confirm the effectiveness of our algorithm.