Overlapping community detection in networks via sparse spectral decomposition

• URL: http://arxiv.org/abs/2009.10641v2
• Date: Mon, 15 Feb 2021 06:43:18 GMT
• Title: Overlapping community detection in networks via sparse spectral decomposition
• Authors: Jes\'us Arroyo, Elizaveta Levina
• Abstract summary: We consider the problem of estimating overlapping community memberships in a network, where each node can belong to multiple communities. Our algorithm is based on sparse principal subspace estimation with iterative thresholding. We show that a fixed point of the algorithm corresponds to correct node memberships under a version of the block model.
• Score: 1.0660480034605242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the problem of estimating overlapping community memberships in a network, where each node can belong to multiple communities. More than a few communities per node are difficult to both estimate and interpret, so we focus on sparse node membership vectors. Our algorithm is based on sparse principal subspace estimation with iterative thresholding. The method is computationally efficient, with a computational cost equivalent to estimating the leading eigenvectors of the adjacency matrix, and does not require an additional clustering step, unlike spectral clustering methods. We show that a fixed point of the algorithm corresponds to correct node memberships under a version of the stochastic block model. The methods are evaluated empirically on simulated and real-world networks, showing good statistical performance and computational efficiency.

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