HFUL: A Hybrid Framework for User Account Linkage across Location-Aware Social Networks

TL;DR

The paper introduces HFUL, a comprehensive framework that enhances user account linkage across location-aware social networks by improving efficiency, effectiveness, scalability, and robustness through innovative indexing, pruning, and similarity measurement techniques.

Contribution

HFUL is the first framework to simultaneously address efficiency, effectiveness, scalability, and robustness in user account linkage across location-aware social networks.

Findings

HFUL outperforms state-of-the-art methods in effectiveness.

HFUL demonstrates superior efficiency and scalability.

HFUL is robust across diverse real-world datasets.

Abstract

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.