In recent years, due to the rise of social, biological, and other rich content graphs, several novel community detection methods using structure and node attributes have been proposed. Moreover, nodes in a network are naturally characterized by multiple community memberships and there is growing interest in overlapping community detection algorithms. In this paper, we design a weighted vertex interaction model based on distance dynamics to divide the network, furthermore, we propose a distance Dynamics-based Overlapping Semantic Community detection algorithm(DOSC) for node-attribute networks. The method is divided into three phases: Firstly, we detect local single-attribute subcommunities in each attribute-induced graph based on the weighted vertex interaction model. Then, a hypergraph is constructed by using the subcommunities obtained in the previous step. Finally, the weighted vertex interaction model is used in the hypergraph to get global semantic communities. Experimental results in real-world networks demonstrate that DOSC is a more effective semantic community detection method compared with state-of-the-art methods.

中文翻译：

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基于距离动力学的节点归属网络重叠语义社区检测

近年来，由于社会，生物和其他内容丰富的图形的兴起，已经提出了几种使用结构和节点属性的新颖的社区检测方法。此外，网络中的节点自然具有多个社区成员资格，并且人们对重叠的社区检测算法越来越感兴趣。本文设计了一种基于距离动力学的加权顶点交互模型对网络进行划分，并针对节点属性网络提出了一种基于距离动力学的重叠语义社区检测算法。该方法分为三个阶段：首先，基于加权顶点交互模型，在每个属性诱导图中检测局部单属性子社区。然后，通过使用上一步中获得的子社区来构造超图。最后，在超图中使用加权顶点交互模型来获得全局语义社区。实际网络中的实验结果表明，与最新技术相比，DOSC是一种更有效的语义社区检测方法。