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.

翻译：社区在理解社交网络中用户行为及网络特征方面扮演着关键角色。部分用户可能同时使用多种社交网络以实现不同目标，这些被称为跨网络桥接的重叠用户。跨多社交网络的社区检测对网络间的交互挖掘、信息扩散及行为迁移分析至关重要。本文提出一种基于非负矩阵三因子分解的多异构社交网络社区检测方法，通过构建公共共识矩阵表示全局融合社区。具体而言，该方法基于网络结构与内容相似性创建邻接矩阵，并构建能够区分不同社交网络中重叠用户的对齐矩阵。利用生成的对齐矩阵，通过检测跨网络重叠用户社区，可增强全局社区的融合程度。基于Twitter、Instagram及Tumblr数据集，本文采用新评价指标验证了所提方法的有效性。实验结果表明，该方法在社区质量与社区融合度方面均展现出优越性能。