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Constraint-Induced Symmetric Nonnegative Matrix Factorization for Accurate Community Detection by Zhigang Liu; Xin Luo; Zidong Wang; Xiaohui Liu is a Computer Science article available to read on EtoBox.

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As a fundamental characteristic of an undirected network, community reveals its networking organization and functional mechanisms, making community detection be a highly-interesting issue in network representation learning. With great interpretability, a symmetric and nonnegative matrix factorization (SNMF)-based approach is frequently adopted to tackle this issue. However, it only adopts a unique feature matrix for describing the symmetry of an undirected network, which unfortunately results in a reduced feature space that evidently impairs its representation learning ability. Motivated by this discovery, this paper proposes a novel Constraint-induced Symmetric Nonnegative Matrix Factorization (C-SNMF) model that adopts three-fold ideas: a) Representing a target undirected network with multiple latent feature matrices, thus preserving its representation learning capacity; b) Incorporating a symmetry-regularizer into its objective function, which preserves the symmetry of the learnt low-rank approximation to the adjacency matrix, thereby making the resultant detector precisely illustrate the target network's symmetry; and c) Introducing a graph-regularizer that preserves local inva

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Author

Zhigang Liu; Xin Luo; Zidong Wang; Xiaohui Liu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)