Alcmean's: Unsupervised community detection using local Laplacian, automatic detection of the number of centers
2026-06-08 • Social and Information Networks
Social and Information NetworksMachine Learning
AI summaryⓘ
The authors introduce ALCMeans, a new method to find groups in complex networks like social or biological systems without needing to decide the number of groups in advance. Their approach uses a special way to pick important nodes automatically and represents nodes with DeepWalk to improve accuracy. Tests show ALCMeans performs better than many popular existing methods on common benchmarks. While it takes more time to run, the authors demonstrate it consistently gives more reliable community detection results.
community detectioncomplex networksLaplacian centralityDeepWalknode embeddingsclustering algorithmsmodularityNMIARIgraph neural networks
Authors
Shahin Momenzadeh, Rojiar Pir Mohammadiani
Abstract
Community detection is a fundamental problem in the analysis of complex networks. It has applications across social, biological, and financial domains. Traditional algorithms such as Louvain, LPA, and modularity optimization often require manual parameter tuning. They also suffer from inaccurate cluster center selection and struggle with scalability. To address these challenges, we propose Automatic Laplacian Centrality Means (ALCMeans), a novel community detection algorithm. ALCMeans combines Laplacian energy-based automatic center identification with DeepWalk embeddings for robust node representation. Unlike existing Laplacian-based and clustering methods, ALCMeans eliminates the need to predefine the number of communities, enhances cluster center selection using structural importance, and leverages representation learning for more accurate and stable assignments. Experimental results on benchmark datasets demonstrate 10 to 20 percent higher NMI and ARI scores compared to Louvain, Newman-Girvan, LPA, Fast-Greedy, and a recent GNN-based competitor (MAGI, KDD 2024). Additional evaluations with modularity and F1-scores confirm the superiority of ALCMeans. Ablation studies highlight the critical contributions of each component. Despite its reliance on DeepWalk parameters and increased runtime relative to lightweight heuristics, ALCMeans consistently outperforms state-of-the-art methods. This makes it a promising tool for real-world network analysis.