Alcmean's: Unsupervised community detection using local Laplacian, automatic detection of the number of centers
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 t...
| Authors | Shahin Momenzadeh, Rojiar Pir Mohammadiani |
|---|---|
| Primary category | cs.SI |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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