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• W2843165098 abstract "One of the most fundamental problems in network study is community detection. The stochastic block model (SBM) is a widely used model, for which various estimation methods have been developed with their community detection consistency results unveiled. However, the SBM is restricted by the strong assumption that all nodes in the same community are stochastically equivalent, which may not be suitable for practical applications. We introduce a pairwise covariates-adjusted stochastic block model (PCABM), a generalization of SBM that incorporates pairwise covariate information. We study the maximum likelihood estimates of the coefficients for the covariates as well as the community assignments. It is shown that both the coefficient estimates of the covariates and the community assignments are consistent under suitable sparsity conditions. Spectral clustering with adjustment (SCWA) is introduced to efficiently solve PCABM. Under certain conditions, we derive the error bound of community detection under SCWA and show that it is community detection consistent. In addition, we investigate model selection in terms of the number of communities and feature selection for the pairwise covariates, and propose two corresponding algorithms. PCABM compares favorably with the SBM or degree-corrected stochastic block model (DCBM) under a wide range of simulated and real networks when covariate information is accessible." @default.
• W2843165098 created "2018-07-19" @default.
• W2843165098 creator A5008605836 @default.
• W2843165098 creator A5044345166 @default.
• W2843165098 date "2018-07-09" @default.
• W2843165098 modified "2023-09-26" @default.
• W2843165098 title "Pairwise Covariates-adjusted Block Model for Community Detection" @default.
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• W2843165098 doi "https://doi.org/10.48550/arxiv.1807.03469" @default.
• W2843165098 hasPublicationYear "2018" @default.
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