Abstract
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.
Abstract (translated)
社区在理解社交网络中的用户行为和网络特征中扮演着关键角色。一些用户可以同时使用多个社交网络来实现各种目标。这些用户被称为重叠用户，他们连接不同的社交网络。在多个社交网络中检测社区对于社交网络中的交互挖掘、信息传播和行为迁移分析至关重要。本文提出了一种基于非负矩阵三元分解的多异质社交网络中的社区检测方法，它构成了一个共同的共识矩阵来表示全局融合社区。具体来说，所提出的方法基于网络结构和内容相似性创建邻接矩阵，然后跟随 alignment matrices 来区分不同社交网络中的重叠用户。通过生成的 alignment matrices，该方法可以通过检测跨网络的重叠用户社区来增强全局社区的融合程度。用 Twitter、Instagram 和 Tumblr 数据集的新指标评估所提出方法的有效性。实验结果表明，其在社区质量和社区融合方面的表现优于其他方法。
URL
https://arxiv.org/abs/2405.04371
