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Spectral Clustering Community Detection Algorithm Based on Point-Wise Mutual Information Graph Kernel.
Chen, Yinan; Ye, Wenbin; Li, Dong.
Afiliación
  • Chen Y; Department of Computer Science and Technology, Shantou University, Shantou 515821, China.
  • Ye W; School of Software Engineering, South China University of Technology, Guangzhou 510006, China.
  • Li D; School of Software Engineering, South China University of Technology, Guangzhou 510006, China.
Entropy (Basel) ; 25(12)2023 Dec 03.
Article en En | MEDLINE | ID: mdl-38136497
ABSTRACT
To address the problem that traditional spectral clustering algorithms cannot obtain the complete structural information of networks, this paper proposes a spectral clustering community detection algorithm, PMIK-SC, based on the point-wise mutual information (PMI) graph kernel. The kernel is constructed according to the point-wise mutual information between nodes, which is then used as a proximity matrix to reconstruct the network and obtain the symmetric normalized Laplacian matrix. Finally, the network is partitioned by the eigendecomposition and eigenvector clustering of the Laplacian matrix. In addition, to determine the number of clusters during spectral clustering, this paper proposes a fast algorithm, BI-CNE, for estimating the number of communities. For a specific network, the algorithm first reconstructs the original network and then runs Monte Carlo sampling to estimate the number of communities by Bayesian inference. Experimental results show that the detection speed and accuracy of the algorithm are superior to other existing algorithms for estimating the number of communities. On this basis, the spectral clustering community detection algorithm PMIK-SC also has high accuracy and stability compared with other community detection algorithms and spectral clustering algorithms.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Entropy (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Entropy (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China