Semantics-Preserving Temporal Adversarial Graph Contrastive Learning for Event Prediction

Event prediction significantly affects public safety, healthcare, and resource optimization. Current methods learn representations of past events through temporal event graphs to predict whether a future event will occur. However, the effectiveness of these methods depends on rich event labels and low-noise data, which are often unattainable in real-world scenarios. In this paper, we propose a Semantics-preserving Temporal Adversarial Graph Contrastive Learning (Sead) method to address this issue. This is achieved by contrasting the clean graph with three auxiliary views (i.e., the semantics-preserving view, the adversarial view and the temporal perturbation view) to learn robust event representations while preserving the associations between events. Specifically, Sead first generates auxiliary views based on the semantic similarity between events and effective perturbations to the temporal event graph. Then, Sead introduces a semantics-aware event encoder that effectively captures the complete semantics of each event. Finally, Sead obtains robust event representations to predict future events. Experimental results demonstrate that our method outperforms current state-of-the-art approaches on real-world datasets. The code is available at https://github.com/hucheng-IIE/Sead .