论文标题
基于正交非负矩阵三因素化的多重网络中的社区检测
Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization
论文作者
论文摘要
网络通常用于建模复杂系统。系统中的不同实体由网络节点及其交互作用表示。在大多数现实生活中，不同的实体可能以不同的方式进行交互，需要使用多重网络使用多个链接来建模交互。推断网络拓扑的主要工具之一是社区检测。尽管单层网络中有许多有关社区检测的作品，但现有的多重网络的社区检测方法主要在跨层次学习共同的社区结构，并且不会考虑整个层的异质性。在本文中，我们介绍了一种新的多重社区检测方法，该方法标识了各个层以及每一层独特的社区。所提出的方法，多重正交非负矩阵三因素化，代表每一层的邻接矩阵，分别是与共同和私人群落相对应的两个低率矩阵分数的总和。与大多数需要预先确定社区数量的现有方法不同，该提议的方法还引入了确定共同和私人社区数量的两阶段方法。对合成和真实的多重网络以及多视图聚类应用程序进行了评估，并与最新技术进行了评估。
Networks are commonly used to model complex systems. The different entities in the system are represented by nodes of the network and their interactions by edges. In most real life systems, the different entities may interact in different ways necessitating the use of multiplex networks where multiple links are used to model the interactions. One of the major tools for inferring network topology is community detection. Although there are numerous works on community detection in single-layer networks, existing community detection methods for multiplex networks mostly learn a common community structure across layers and do not take the heterogeneity across layers into account. In this paper, we introduce a new multiplex community detection method that identifies communities that are common across layers as well as those that are unique to each layer. The proposed method, Multiplex Orthogonal Nonnegative Matrix Tri-Factorization, represents the adjacency matrix of each layer as the sum of two low-rank matrix factorizations corresponding to the common and private communities, respectively. Unlike most of the existing methods, which require the number of communities to be pre-determined, the proposed method also introduces a two stage method to determine the number of common and private communities. The proposed algorithm is evaluated on synthetic and real multiplex networks, as well as for multiview clustering applications, and compared to state-of-the-art techniques.
