K-Means Community Detection Algorithm Based on Density Peaks

TL;DR

This paper introduces a new community detection algorithm that automatically identifies network communities without needing pre-defined parameters, showing better performance and efficiency on benchmark and real-world networks.

Contribution

The novel D-means algorithm combines density peak clustering with K-means to automatically determine community centers and improve robustness in community detection.

Findings

The D-means algorithm outperforms traditional methods in ACC, ARI, and NMI metrics on benchmark and real-world networks.

It achieves improved runtime efficiency and robustness in identifying community structures.

Application to Urumqi's public transport network identified 12 meaningful communities for urban planning.

Abstract

The identification of community structure is pivotal for understanding the functional characteristics of complex networks. To address the limitations of most existing community detection algorithms, which often require predefining the number of communities and lack robustness, this paper proposes a novel community detection algorithm named D-means (K-means community detection algorithm based on density peaks). This algorithm integrates the concept of density peak clustering with K-means spectral clustering, employing Chebyshev’s inequality to automatically determine the number of community centers, thereby enabling unsupervised identification of community quantities. By designing a multi-dimensional evaluation framework, the comparative experiments were conducted on LFR benchmark networks (Lancichinetti-Fortunato-Radicchi benchmark networks) and real-world social network datasets. The results demonstrate that the D-means algorithm outperforms traditional algorithms in terms of ACC (accuracy), ARI (adjusted rand index), and NMI (normalized mutual information) metrics, while also achieving improvements in runtime efficiency, showcasing strong robustness. Finally, the D-means algorithm was applied to the public transportation network of Urumqi. Empirical analysis identified 12 functionally significant transportation communities, providing theoretical support for urban rail transit optimization and commercial facility layout planning.

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