Network alignment in multiplex social networks using the information diffusion dynamics

Abstract

Modern social networking applications (SNAs) typically rely on independent data management systems, leading to fragmented user identities and the creation of data silos. This fragmentation impedes the development of unified user profiles and limits the effectiveness of cross-platform behavioural analysis and personalized recommendations. As the demand for integrated services grows, the need for a unified user identity across platforms becomes increasingly critical. However, existing identity integration methods face significant challenges, including data isolation, privacy risks, and a lack of universal standards. To address these issues, we propose a framework that utilizes node dynamics time-series data for network alignment in multiplex social networks. Our approach employs the UIU diffusion model to simulate information diffusion dynamics across multiple platforms and uses the Expectation-Maximization (EM) algorithm for network alignment. Crucially, our method relies solely on publicly available user information from different platforms, avoiding the need for access to private user data, thereby enhancing security. Experimental results demonstrate the model’s efficacy, achieving F1 scores of 100% for interlayer links and over 90% for intralayer links on real-world datasets. These findings highlight the model’s potential for applications in social influence analysis, community detection, and recommendation systems.