Consensus clustering in complex networks

TL;DR

This paper introduces a consensus clustering framework that improves the stability and accuracy of community detection in complex networks, adaptable to temporal networks and capable of tracking evolving community structures.

Contribution

It presents a self-consistent consensus clustering method that enhances existing community detection algorithms and monitors community evolution over time.

Findings

Enhanced stability and accuracy of community detection.

Effective tracking of community evolution in temporal networks.

Demonstrated application on a large citation network.

Abstract

The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Here we show that consensus clustering can be combined with any existing method in a self-consistent way, enhancing considerably both the stability and the accuracy of the resulting partitions. This framework is also particularly suitable to monitor the evolution of community structure in temporal networks. An application of consensus clustering to a large citation network of physics papers demonstrates its capability to keep track of the birth, death and diversification of topics.