Subsampling spectral clustering for stochastic block models in large-scale networks

Jiayi Deng, Danyang Huang, Yi Ding, Yingqiu Zhu, Bingyi Jing and Bo Zhang

Computational Statistics & Data Analysis, 2024, vol. 189, issue C

Abstract: The rapid development of science and technology has generated large amounts of network data, leading to significant computational challenges for network community detection. A novel subsampling spectral clustering algorithm is proposed to address this issue, which aims to identify community structures in large-scale networks with limited computing resources. The algorithm constructs a subnetwork by simple random subsampling from the entire network, and then extends the existing spectral clustering to the subnetwork to estimate the community labels for entire network nodes. As a result, for large-scale datasets, the method can be realized even using a personal computer. Moreover, the proposed method can be generalized in a parallel way. Theoretically, under the stochastic block model and its extension, the degree-corrected stochastic block model, the theoretical properties of the subsampling spectral clustering method are correspondingly established. Finally, to illustrate and evaluate the proposed method, a number of simulation studies and two real data analyses are conducted.

Keywords: Large-scale networks; Community detection; Spectral clustering; Network subsampling; Stochastic block model (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:189:y:2024:i:c:s0167947323001469

DOI: 10.1016/j.csda.2023.107835

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