Integrating User Relationships and Features for Intelligence of Social Things Aware Information Diffusion Prediction
Abstract
In the intelligence of social things (IoST) paradigm, where interconnected devices and social networks create a dynamic ecosystem, understanding information diffusion is essential. IoST integrates user interactions, device behaviors, and contextual factors, adding complexity to information networks and necessitating accurate prediction models. This work analyses user behavior in terms of both group and individual relationships and presents an information propagation prediction model that combines information propagation topology features with user relationship representations. Information diffusion prediction analyzes patterns of spread in networks to understand and forecast propagation processes. Existing studies emphasize social and dynamic influence relationships within user groups but often neglect user similarity in group relations and intrinsic factors affecting individual sharing decisions. To address these gaps, a novel model is proposed, combining user relationship representations and diffusion topological features. At the group level, a user cooccurrence graph captures similarity relationship, integrating these with diffusion topology to analyze group interactions. At the individual level, user-specific feature representations and influence factor vectors address intrinsic motivations for sharing. Experimental results validate the model’s efficacy, achieving performance improvements on public datasets. On the Memetracker dataset, the model increased MAP@k by 6.54% and hits@k by 2.75%, demonstrating its ability to capture both group and individual dynamics for enhanced diffusion prediction.