analiza aiqbench
ALCMeans addresses key limitations in traditional community detection by eliminating the need for manual parameter tuning and automatically determining the number of clusters. By integrating Laplacian energy-based center identification with DeepWalk embeddings, the method achieves a 10–20% improvement in NMI and ARI scores over established baselines like Louvain and recent GNN competitors. While it incurs higher runtime costs compared to lightweight heuristics, its superior accuracy and stability make it a robust option for complex network analysis in social, biological, and financial domains.
Alcmean's: Unsupervised community detection using local Laplacian, automatic detection of the number of centers
arXiv:2606.09100v1 Announce Type: cross 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.
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.
Social and Information Networks (cs.SI); Machine Learning (cs.LG)
arXiv:2606.09100 [cs.SI]
(or arXiv:2606.09100v1 [cs.SI] for this version)
https://doi.org/10.48550/arXiv.2606.09100 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.22103/jmmr.2026.25756.1849 Focus to learn more DOI(s) linking to related resources
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