IEGSCL: Interaction-Enhanced Graph Neural Sequence Contrastive Learning for Microscopic Diffusion Prediction

Understanding the complex relationships and behavioral preferences among users in social networks is crucial for elucidating the mechanisms of information diffusion. While recent information diffusion prediction methods, empowered by graph neural networks, have advanced the learning of user representations, they rarely exploit spread interaction feedback. This feedback reflects users' interests in engaging with information and serves as a key driver of information diffusion. Moreover, the underutilization of unlabeled data leads to an overreliance on labeled data, consequently constraining the model's self-learning and generalization capabilities. To address these limitations, we propose a novel microscopic diffusion prediction model based on interaction-enhanced graph neural sequence contrastive learning (IEGSCL). Specifically, we construct a triple graph to explore the diversity of user relationships and preferences through the lenses of trust and interaction. A self-supervised graph contrastive learning module is designed to transfer user intents, maximizing the utility of unlabeled data and tackling the feature extraction challenge. Furthermore, we devise an information-driven gating strategy that adaptively modulates the contributions of social and interactive intents to cascade participation, thereby effectively integrating interaction feedback into the cascade modeling. Finally, we employ maximum mean discrepancy (MMD) to enforce distributional consistency between global relationship representations and local cascade encodings. Extensive experiments on four public datasets validate the superior performance of the proposed model over existing baselines.