DyGCL: Dynamic Graph Contrastive Learning For Event Prediction

Predicting events, ranging from political unrest to disease outbreaks and criminal activities, stands as a pivotal task in proactively addressing emerging challenges. Despite the richness of textual data as a source for event detection, it is challenging to extract contextual information from documents due to their complex structure and the dynamic evolution of events. In response to this challenge, dynamic Graph Neural Networks (GNNs) have emerged as a promising tool for capturing the intricate patterns embedded within textual data graphs. Nevertheless, many models in this domain primarily rely on local node-level representations, overlooking the essential global graph-level context. However, both node-level and graph-level representations are critical for effective event prediction. Node-level representations provide insight into the local structure, while graph-level representations offer an understanding of the global structure and the evaluation of temporal graphs. To address these challenges, in this paper, we propose a Dynamic Graph Contrastive Learning (DyGCL) method for event prediction. Our model DyGCL first employs a local view encoder to effectively capture the local dynamic structure of input graphs as the evolving node representations. Then, it performs a global view encoder to perceive the hierarchical dynamic graph representation of the input graphs. Finally, the graph representations from both encoders, optimized via contrastive learning, are combined with an attention mechanism and utilized to predict future events. Our extensive experiments demonstrate that our proposed method outperforms the state-of-the-art methods for event prediction on six real-world datasets.