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

2025-02-18

Proceedings of the 3rd International Conference on Engineering and Innovative Technology

AI Summary

Community detection in complex social networks faces challenges including structural non-convexity, unknown number of communities, and sensitivity to initialization. To address these, this paper proposes ELPMeans—an unsupervised method that innovatively integrates Laplacian centrality with a minimum-distance metric to automatically identify robust central nodes. It further combines hierarchical clustering with K-means optimization to adaptively determine the number of communities and enhance clustering quality. Unlike conventional K-means, ELPMeans eliminates the need for pre-specifying K, avoids bias from random initialization, and effectively detects non-convex community structures. Extensive experiments on multiple real-world network datasets demonstrate that ELPMeans achieves significantly higher accuracy than recent state-of-the-art methods, while exhibiting lower time complexity and strong generalizability—requiring no prior knowledge or parameter tuning.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Clustering
• Search and Optimization: Non-convex Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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 non-convex shapes and no prior knowledge about the number of communities present.

Problem

• Detects communities in complex social networks
• Uses Laplacian centrality and clustering
• Improves accuracy and reduces time complexity

Innovation

• Combines Laplacian and K-means algorithms
• Uses Laplacian centrality for node identification
• Unsupervised method for community detection