Estimating Mixed-Memberships Using the Symmetric Laplacian Inverse Matrix

• URL: http://arxiv.org/abs/2012.09561v2
• Date: Fri, 5 Apr 2024 08:09:39 GMT
• Title: Estimating Mixed-Memberships Using the Symmetric Laplacian Inverse Matrix
• Authors: Huan Qing, Jingli Wang,
• Abstract summary: Mixed-SLIM is a spectral clustering method on the symmetrized Laplacian inverse matrix under the degree-corrected mixed membership model. We provide theoretical bounds for the estimation error on the proposed algorithm and its regularized version under mild conditions. These Mixed-SLIM methods outperform state-of-art methods in simulations and substantial empirical datasets for both community detection and mixed membership community detection problems.
• Score: 0.4972323953932129
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mixed membership community detection is a challenging problem. In this paper, to detect mixed memberships, we propose a new method Mixed-SLIM which is a spectral clustering method on the symmetrized Laplacian inverse matrix under the degree-corrected mixed membership model. We provide theoretical bounds for the estimation error on the proposed algorithm and its regularized version under mild conditions. Meanwhile, we provide some extensions of the proposed method to deal with large networks in practice. These Mixed-SLIM methods outperform state-of-art methods in simulations and substantial empirical datasets for both community detection and mixed membership community detection problems.

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