Information diffusion prediction, the forecasting of propagation paths, provides critical insights into information spread mechanisms, directly enabling applications like misinformation spread forecasting and detection for malicious account. Prior research primarily focused on combining user social graphs and information cascades for prediction, often overlooking the distinct role characteristics users exhibit during interactions. Classifying users into different roles enables the construction of a multi-layered social graph, facilitating the extraction of deeper user features. This paper introduces a model that leverages multi-dimensional interactions between user features. Specifically, to account for users' dynamic preferences, we construct sequential hypergraphs from information cascades using timestamps and utilize a hypergraph neural network to extract users' dynamic features. Furthermore, to capture users' static features, we build multi-layer social networks from the social graph based on users' roles. We employ graph convolutional networks to separately extract static features from each layer and subsequently fuse them using an attention mechanism. Superior performance of our framework is evidenced by experimental validation on real-world datasets against cutting-edge benchmarks.

Information diffusion prediction is a crucial task for comprehending the dissemination process of information. Although this problem has received significant attention recently, most of the state-of-the-arts primarily focus on the modelling of information cascades, while neglecting the implicit social relations between users in the social network and failing to adequately model the interrelations between the user social network and information cascades. To tackle the aforementioned issues, in this work, we propose aDual-StateHypergraphContrastiveLearning model (DSHCL). Specifically, we first propose to construct a social hypergraph based on the social network to capture the implicit social relations. Then, for capturing the cascade level correlations among users, we generate the dual-state (i.e., static and dynamic) user representations from the user social hypergraph and information cascades. Finally, we exploit contrastive learning to model the interplay between the social network and information cascades by discriminating the dual-state representations generated from them. We conduct an empirical assessment of DSHCL across four publicly available datasets, and the findings underscore the DSHCL's superiority and the efficacy of its components.