Robust network community detection using balanced propagation

TL;DR

This paper introduces a balanced propagation method for network community detection that enhances robustness and stability over traditional label propagation, while maintaining simplicity and scalability.

Contribution

It proposes a novel balanced propagation algorithm that counteracts randomness in node update order, improving robustness and performance in community detection.

Findings

Balanced propagation is more robust than label propagation.

The method improves community detection stability.

Performance is enhanced on synthetic and real networks.

Abstract

Label propagation has proven to be an extremely fast method for detecting communities in large complex networks. Furthermore, due to its simplicity, it is also currently one of the most commonly adopted algorithms in the literature. Despite various subsequent advances, an important issue of the algorithm has not yet been properly addressed. Random (node) update orders within the algorithm severely hamper its robustness, and consequently also the stability of the identified community structure. We note that an update order can be seen as increasing propagation preferences from certain nodes, and propose a balanced propagation that counteracts for the introduced randomness by utilizing node balancers. We have evaluated the proposed approach on synthetic networks with planted partition, and on several real-world networks with community structure. The results confirm that balanced propagation is significantly more robust than label propagation, when the performance of community detection is even improved. Thus, balanced propagation retains high scalability and algorithmic simplicity of label propagation, but improves on its stability and performance.