Community detection is a fundamental research problem in social networks. However, most existing research focuses on homogeneous networks while ignoring the multitopology and attributes in social media. In this article, we propose community detection algorithms based on community kernels to detect high-quality communities in heterogeneous social networks. It is noticed that the social community has multiple topology structures, as nodes or users in social media networks have multiple attributions. For example, users can be friends and coworkers in a research group simultaneously. Hence, we propose a multilayer and attribute combined measure (MACM), a novel measurement based on the multilayer structure and common neighboring attributes, which includes the similarity measure between nodes and the importance measure for individual node in multilayer networks. Two improved community kernel detection algorithms based on MACM are subsequently proposed. They are the MA-Greedy, which is based on the greedy algorithm, and the MA-WeBA, which is a weighted balanced algorithm. The multilayer structure and attributes are comprehensively considered when calculating the similarity and importance of nodes in these strategies. Extensive experimental results on two public data sets demonstrate that the multilayer structure and attribute information can be used to enhance the precision of community detection.

中文翻译：

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使用社交网络中的多拓扑和属性进行社区检测

社区检测是社交网络中的一个基础研究问题。然而，现有的大多数研究都集中在同构网络上，而忽略了社交媒体中的多拓扑和属性。在本文中，我们提出了基于社区内核的社区检测算法，以检测异构社交网络中的高质量社区。值得注意的是，社交社区具有多种拓扑结构，因为社交媒体网络中的节点或用户具有多种属性。例如，用户可以同时是研究组中的朋友和同事。因此，我们提出了一种多层和属性组合测量（MACM），一种基于多层结构和公共相邻属性的新型测量，其中包括节点之间的相似性度量和多层网络中单个节点的重要性度量。随后提出了两种改进的基于MACM的社区核检测算法。它们是基于贪心算法的 MA-Greedy 和加权平衡算法 MA-WeBA。这些策略在计算节点的相似度和重要性时综合考虑了多层结构和属性。在两个公共数据集上的广泛实验结果表明，多层结构和属性信息可用于提高社区检测的精度。这是一种加权平衡算法。这些策略在计算节点的相似度和重要性时综合考虑了多层结构和属性。在两个公共数据集上的大量实验结果表明，多层结构和属性信息可用于提高社区检测的精度。这是一种加权平衡算法。这些策略在计算节点的相似度和重要性时综合考虑了多层结构和属性。在两个公共数据集上的大量实验结果表明，多层结构和属性信息可用于提高社区检测的精度。