Community detection, aiming at determining correct affiliation of each node in a network, is a critical task of complex network analysis. Owing to its high efficiency, Symmetric and Non-negative Matrix Factorization (SNMF) is frequently adopted to handle this task. However, existing SNMF models mostly focus on a network's first-order topological information described by its adjacency matrix without considering the implicit associations among involved nodes. To address this issue, this study proposes a Pointwise mutual information-incorporated and Graph-regularized SNMF (PGS) model. It uses a) Pointwise Mutual Information to quantify implicit associations among nodes, thereby completing the missing but crucial information among critical nodes in a uniform way; b) graph-regularization to achieve precise representation of local topology, and c) SNMF to implement efficient community detection. Empirical studies on eight real-world social networks generated by industrial applications demonstrate that a PGS model achieves significantly higher accuracy gain in community detection than state-of-the-art community detectors.

中文翻译：

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通过结合点向互信息的对称非负矩阵分解实现高精度的社区检测

旨在确定网络中每个节点的正确隶属关系的社区检测是复杂网络分析的关键任务。由于效率高，因此经常采用对称和非负矩阵分解（SNMF）来处理此任务。但是，现有的SNMF模型主要集中在由其邻接矩阵描述的网络的一阶拓扑信息上，而不考虑相关节点之间的隐式关联。为了解决这个问题，本研究提出了一种结合点向互信息和图正则化的SNMF（PGS）模型。它使用以下方法：a）点向互信息量化节点之间的隐式关联，从而以统一的方式完成关键节点之间缺失但关键的信息；b）图正则化，以实现局部拓扑的精确表示；c）SNMF实施有效的社区检测。对由工业应用程序生成的八个现实世界社交网络的经验研究表明，PGS模型在社区检测方面的获取准确度要比最新的社区检测器高得多。