Controlled Label Propagation: Preventing Over-Propagation through Gradual Expansion

TL;DR

This paper introduces a controlled label propagation method that gradually expands community capacities to prevent over-propagation, enhancing community detection accuracy in complex networks.

Contribution

It proposes a novel extension to label propagation that manages community growth over time, improving detection in ambiguous and clear community structures.

Findings

Improves detection in networks with unclear community boundaries.

Enhances overall quality of community detection in complex networks.

Preliminary results show better performance than classic label propagation.

Abstract

Identifying communities has always been a fundamental task in analysis of complex networks. Many methods have been devised over the last decade for detection of communities. Amongst them, the label propagation algorithm brings great scalability together with high accuracy. However, it has one major flaw; when the community structure in the network is not clear enough, it will assign every node the same label, thus detecting the whole graph as one giant community. We have addressed this issue by setting a capacity for communities, starting from a small value and gradually increasing it over time. Preliminary results show that not only our extension improves the detection capability of classic label propagation algorithm when communities are not clearly detectable, but also improves the overall quality of the identified clusters in complex networks with a clear community structure.