Fast Complex Network Clustering Algorithm Using Agents

TL;DR

This paper introduces a fast, agent-based network clustering algorithm that optimizes local modularity to efficiently cluster large-scale networks with high quality, outperforming existing methods.

Contribution

It presents a novel local-view, agent-based clustering algorithm that improves efficiency and quality for large-scale network clustering tasks.

Findings

Effective on both computer-generated and real-world networks

Achieves high clustering quality compared to existing algorithms

Capable of handling large-scale networks efficiently

Abstract

Recently, the sizes of networks are always very huge, and they take on distributed nature. Aiming at this kind of network clustering problem, in the sight of local view, this paper proposes a fast network clustering algorithm in which each node is regarded as an agent, and each agent tries to maximize its local function in order to optimize network modularity defined by function Q, rather than optimize function Q from the global view as traditional methods. Both the efficiency and effectiveness of this algorithm are tested against computer-generated and real-world networks. Experimental result shows that this algorithm not only has the ability of clustering large-scale networks, but also can attain very good clustering quality compared with the existing algorithms. Furthermore, the parameters of this algorithm are analyzed.