Community detection using multitopology and attributes in social networks

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.