User Identity Linkage across Social Networks with the Enhancement of Knowledge Graph and Time Decay Function

Users participate in multiple social networks for different services. User identity linkage aims to predict whether users across different social networks refer to the same person, and it has received significant attention for downstream tasks such as recommendation and user profiling. Recently, researchers proposed measuring the relevance of user-generated content to predict identity linkages of users. However, there are two challenging problems with existing content-based methods: first, barely considering the word similarities of texts is insufficient where the semantical correlations of named entities in the texts are ignored; second, most methods use time discretization technology, where the texts are divided into different time slices, resulting in failure of relevance modeling. To address these issues, we propose a user identity linkage model with the enhancement of a knowledge graph and continuous time decay functions that are designed for mitigating the influence of time discretization. Apart from modeling the correlations of the words, we extract the named entities in the texts and link them into the knowledge graph to capture the correlations of named entities. The semantics of texts are enhanced through the external knowledge of the named entities in the knowledge graph, and the similarity discrimination of the texts is also improved. Furthermore, we propose continuous time decay functions to capture the closeness of the posting time of texts instead of time discretization to avoid the matching error of texts. We conduct experiments on two real public datasets, and the experimental results show that the proposed method outperforms state-of-the-art methods.     

基于信号理论的社交媒体平台安全和信任评估度量方法

开放的社交媒体平台给用户带来使用便捷的同时,也存在不少恶意站点、信息欺骗和信任缺失等安全性和可信性问题。社交平台的安全性和可信性作为社会交互的基础,在信息共享与交流中至关重要。传统的安全和信任评估仅关注于用户间的信任关系以及安全实现,而针对社交媒体平台的评估和度量方法还不健全。因此提出了一种基于信息管理信号理论的在线社交网络平台安全和信任度量方法。首先,对平台安全性和可信性信号进行分类,并采用OWL语言和时态逻辑形式化描述了平台静态属性和动态行为特征。其次,使用FAHP确定此类信号的指标权重并进行系统的评价,并结合群体计算思想提出了一个平台安全和信任的综合评估计算模型。最后,在一个现实的多媒体社交网络平台(CyVOD.net)上进行了评估实验。实验结果显示,该方法能够准确地获得社交平台的各安全和信任要素的评估值,并有效地指导社交媒体网络平台的功能进化和版本更新。