Community Detection for Heterogeneous Multiple Social Networks
arxiv(2024)
摘要
The community plays a crucial role in understanding user behavior and network
characteristics in social networks. Some users can use multiple social networks
at once for a variety of objectives. These users are called overlapping users
who bridge different social networks. Detecting communities across multiple
social networks is vital for interaction mining, information diffusion, and
behavior migration analysis among networks. This paper presents a community
detection method based on nonnegative matrix tri-factorization for multiple
heterogeneous social networks, which formulates a common consensus matrix to
represent the global fused community. Specifically, the proposed method
involves creating adjacency matrices based on network structure and content
similarity, followed by alignment matrices which distinguish overlapping users
in different social networks. With the generated alignment matrices, the method
could enhance the fusion degree of the global community by detecting
overlapping user communities across networks. The effectiveness of the proposed
method is evaluated with new metrics on Twitter, Instagram, and Tumblr
datasets. The results of the experiments demonstrate its superior performance
in terms of community quality and community fusion.
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