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Qiong, Zheng

A signal-diffusion-based spectral clustering method for community detection. (English) Zbl 1435.62245

Int. J. Wavelets Multiresolut. Inf. Process. 18, No. 1, Article ID 1941019, 19 p. (2020).
Summary: As the traditional spectral community detection method uses adjacency matrix for clustering which might cause the problem of accuracy reduction, we proposed a signal-diffusion-based spectral clustering for community detection. This method solves the problem that unfixed total signal as using the signal transmission mechanism, provides optimization of algorithm time complexity, improves the performance of spectral clustering with construction of Laplacian based on signal diffusion. Experiments prove that the method reaches as better performance on real-world network and Lancichinetti-Fortunato-Radicchi (LFR) benchmark.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
62M15 Inference from stochastic processes and spectral analysis

Keywords:

community detection; spectral clustering; signal diffusion; Laplacian matrix; signal transmission
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References:

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