Community Detection by ELPMeans: An Unsupervised Approach That Uses Laplacian Centrality and Clustering

TL;DR

ELPMeans is an unsupervised community detection algorithm that combines Laplacian centrality, hierarchical clustering, and K-means to improve accuracy and efficiency in identifying communities in complex networks.

Contribution

It introduces a novel unsupervised method that effectively detects communities without prior knowledge of their number, addressing common initialization issues.

Findings

Improves accuracy over recent methods

Reduces computational time significantly

Handles nonconvex community shapes

Abstract

Community detection in network analysis has become more intricate due to the recent hike in social networks (Cai et al., 2024). This paper suggests a new approach named ELPMeans that strives to address this challenge. For community detection in the whole network, ELPMeans combines Laplacian, Hierarchical Clustering as well as K-means algorithms. Our technique employs Laplacian centrality and minimum distance metrics for central node identification while k-means learning is used for efficient convergence to final community structure. Remarkably, ELPMeans is an unsupervised method which is not only simple to implement but also effectively tackles common problems such as random initialization of central nodes, or finding of number of communities (K). Experimental results show that our algorithm improves accuracy and reduces time complexity considerably outperforming recent approaches on real world networks. Moreover, our approach has a wide applicability range in various community detection tasks even with nonconvex shapes and no prior knowledge about the number of communities present.