Network community detection via iterative edge removal in a flocking-like system

TL;DR

This paper introduces a nature-inspired, iterative edge removal method for network community detection, leveraging alignment dynamics to identify communities efficiently in large-scale networks.

Contribution

The paper proposes a novel community detection technique based on flocking-like dynamics and iterative edge removal, demonstrating robustness and scalability.

Findings

Method performs well on benchmark networks.

Algorithm is robust across various network types.

Runs in quasilinear time for large sparse networks.

Abstract

We present a network community-detection technique based on properties that emerge from a nature-inspired system of aligning particles. Initially, each vertex is assigned a random-direction unit vector. A nonlinear dynamic law is established so that neighboring vertices try to become aligned with each other. After some time, the system stops and edges that connect the least-aligned pairs of vertices are removed. Then the evolution starts over without the removed edges, and after enough number of removal rounds, each community becomes a connected component. The proposed approach is evaluated using widely-accepted benchmarks and real-world networks. Experimental results reveal that the method is robust and excels on a wide variety of networks. Moreover, for large sparse networks, the edge-removal process runs in quasilinear time, which enables application in large-scale networks.