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

arXiv: https://arxiv.org/abs/2502.19895 · HTML: https://mlsanity.com/paper/2502.19895/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 1 of 1)

- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)

## Built on by (most cited first, 1 of 1)

- [Alcmean's: Unsupervised community detection using local Laplacian, automatic detection of the number of centers](https://mlsanity.com/paper/2606.09100/) (2026)

## Abstract (arXiv)

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.