Lu Jing, Gai Qikai
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.
PeerJ Comput Sci. 2024 Feb 28;10:e1892. doi: 10.7717/peerj-cs.1892. eCollection 2024.
The user alignment of cross-social networks is divided into user and group alignments, respectively. Obtaining users' full features is difficult due to social network privacy protection policies in user alignment mode. In contrast, the alignment accuracy is low due to the large number of edge users in the group alignment mode. To resolve this issue, First, stable topics are obtained from user-generated content (UGC) based on embedded topic jitter time, and the weight of user edges is updated by using vector distances. An improved Louvain algorithm, called Stable Topic-Louvain (ST-L), is designed to accomplish multi-level community detection without predetermined tags. It aims to obtain fuzzy topic features of the community and finalize the community alignment across social networks. Furthermore, iterative alignment is executed from coarse-grained communities to fine-grained sub-communities until user-level alignment occurs. The process can be terminated at any layer to achieve multi-granularity alignment, which resolves the low accuracy issue of edge user alignment at a single granularity and improves the accuracy of user alignment. The effectiveness of the proposed method is shown by implementing real datasets.
跨社交网络的用户对齐分别分为用户对齐和群组对齐。在用户对齐模式下，由于社交网络隐私保护政策，获取用户的完整特征很困难。相比之下，在群组对齐模式下，由于边缘用户数量众多，对齐精度较低。为了解决这个问题，首先，基于嵌入式主题抖动时间从用户生成内容（UGC）中获取稳定主题，并使用向量距离更新用户边的权重。设计了一种改进的Louvain算法，称为稳定主题-Louvain（ST-L），用于在没有预定标签的情况下完成多层次社区检测。其目的是获取社区的模糊主题特征，并最终确定跨社交网络的社区对齐。此外，从粗粒度社区到细粒度子社区执行迭代对齐，直到发生用户级对齐。该过程可以在任何一层终止以实现多粒度对齐，这解决了单粒度边缘用户对齐精度低的问题，并提高了用户对齐的准确性。通过实现真实数据集展示了所提方法的有效性。
