Community detection in multi-layer networks by regularized debiased spectral clustering
<|reference_start|>Community detection in multi-layer networks by regularized debiased spectral clustering: Community detection is a crucial problem in the analysis of multi-layer networks. In this work, we introduce a new method, called regularized debiased sum of squared adjacency matrices (RDSoS), to detect latent communities in multi-layer networks. RDSoS is developed based on a novel regularized Laplacian matrix that regularizes the debiased sum of squared adjacency matrices. In contrast, the classical regularized Laplacian matrix typically regularizes the adjacency matrix of a single-layer network. Therefore, at a high level, our regularized Laplacian matrix extends the classical regularized Laplacian matrix to multi-layer networks. We establish the consistency property of RDSoS under the multi-layer stochastic block model (MLSBM) and further extend RDSoS and its theoretical results to the degree-corrected version of the MLSBM model. The effectiveness of the proposed methods is evaluated and demonstrated through synthetic and real datasets.<|reference_end|>
@article{qing2024community,
    title={Community detection in multi-layer networks by regularized debiased
  spectral clustering},
    author={Huan Qing},
    journal={arXiv preprint arXiv:2409.07956},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07956},
    primaryClass={stat.ME cs.SI}
}