论文标题
高阶近端近端结合，对称性和图形规范化的非负矩阵分解用于社区检测
High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized Nonnegative Matrix Factorization for Community Detection
论文作者
论文摘要
社区描述了网络的功能机制，使社区检测是各种真实应用（例如发现社交圈）的基本图形工具。迄今为止，由于其高解释性和可伸缩性，经常采用对称和非负矩阵分解（SNMF）模型来解决此问题。但是，大多数现有的基于SNMF的社区检测方法忽略了网络中的高阶连接模式。在本文中，在本文中，我们提出了一种高阶（HOP）结合，对称性和图形调查的NMF（HSGN）模型，该模型采用以下三个想法：a）采用加权点上的相互信息（PMI），以基于基于网络中NODE的基于基于网络的HOP索引。 b）利用迭代重建方案将捕获的跳跃编码到网络中； c）引入对称性和图表的NMF算法，以准确检测社区。对八个现实世界网络的广泛实证研究表明，基于HSGN的社区探测器在提供高度准确的社区检测结果方面显着优于基准和最先进的社区探测器。
Community describes the functional mechanism of a network, making community detection serve as a fundamental graph tool for various real applications like discovery of social circle. To date, a Symmetric and Non-negative Matrix Factorization (SNMF) model has been frequently adopted to address this issue owing to its high interpretability and scalability. However, most existing SNMF-based community detection methods neglect the high-order connection patterns in a network. Motivated by this discovery, in this paper, we propose a High-Order Proximity (HOP)-incorporated, Symmetry and Graph-regularized NMF (HSGN) model that adopts the following three-fold ideas: a) adopting a weighted pointwise mutual information (PMI)-based approach to measure the HOP indices among nodes in a network; b) leveraging an iterative reconstruction scheme to encode the captured HOP into the network; and c) introducing a symmetry and graph-regularized NMF algorithm to detect communities accurately. Extensive empirical studies on eight real-world networks demonstrate that an HSGN-based community detector significantly outperforms both benchmark and state-of-the-art community detectors in providing highly-accurate community detection results.
