当我们在不同平台或设备上链接属于同一用户的用户帐户时，补充信息的来源是相互关联的。扩展的信息促进了跨平台预测、跨平台推荐、广告等广泛应用的发展。由于用户账户关联的重要性和支持GPS的移动设备的广泛普及，越来越多的研究将用户账户与跨位置感知社交网络的时空数据关联起来。与大多数现有的研究只关注有效性不同，我们提出了一个新的框架HFUL（A Hybrid Framework for User Account Linkage across Location-Aware社交网络），其中考虑了用户帐户链接的效率，有效性，可扩展性，鲁棒性和应用。具体而言，为了提高效率，我们开发了一个全面的索引结构，从时空的角度来看，设计新颖的修剪策略，以减少搜索空间。为了提高算法的有效性，提出了一种基于核密度估计的用户相似性度量方法。此外，我们调查的应用HFUL在用户预测，时间预测和位置预测。在三个真实数据集上进行的大量实验证明了HFUL在有效性、效率、可扩展性、鲁棒性和应用方面的优越性。
英文摘要：
Sources of complementary information are connected when we link user accounts belonging to the same user across different platforms or devices. The expanded information promotes the development of a wide range of applications, such as cross-platform prediction, cross-platform recommendation, and advertisement. Due to the significance of user account linkage and the widespread popularization of GPS-enabled mobile devices, there are increasing research studies on linking user account with spatio-temporal data across location-aware social networks. Being different from most existing studies in this domain that only focus on the effectiveness, we propose a novel framework entitled HFUL (A Hybrid Framework for User Account Linkage across Location-Aware Social Networks), where efficiency, effectiveness, scalability, robustness, and application of user account linkage are considered. Specifically, to improve the efficiency, we develop a comprehensive index structure from the spatio-temporal perspective, and design novel pruning strategies to reduce the search space. To improve the effectiveness, a kernel density estimation-based method has been proposed to alleviate the data sparsity problem in measuring users' similarities. Additionally, we investigate the application of HFUL in terms of user prediction, time prediction, and location prediction. The extensive experiments conducted on three real-world datasets demonstrate the superiority of HFUL in terms of effectiveness, efficiency, scalability, robustness, and application compared with the state-of-the-art methods.
Locate Who You Are: Matching Geo-location to Text for User Identity Linkage
Data Intelligence System Research Center, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所数据智能系统研究中心）；University of Chinese Academy of Sciences（中国科学院大学）；National Engineering Lab for Risk Perception and Prevention, China Academic Electronic and Information Technology（中国电子科技集团公司风险感知与预防国家工程实验室）
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间安全学院）；Sony AI（索尼人工智能）；Salesforce（赛富时）；Zhejiang University（浙江大学）；Macquarie University（麦考瑞大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）
