Event-Aware Prompt Learning for Dynamic Graphs
This work addresses a specific bottleneck in dynamic graph learning for domains like social networks or recommendation systems, but it is incremental as it builds on existing prompt learning methods.
The paper tackles the problem of existing dynamic graph prompt learning methods overlooking historical events by proposing EVP, an event-aware framework that enhances existing methods' ability to leverage historical knowledge, achieving improved performance on four public datasets.
Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph learning, dynamic graph neural networks (DGNNs) have emerged as popular solutions. Recently, prompt learning methods have been explored on dynamic graphs. However, existing methods generally focus on capturing the relationship between nodes and time, while overlooking the impact of historical events. In this paper, we propose EVP, an event-aware dynamic graph prompt learning framework that can serve as a plug-in to existing methods, enhancing their ability to leverage historical events knowledge. First, we extract a series of historical events for each node and introduce an event adaptation mechanism to align the fine-grained characteristics of these events with downstream tasks. Second, we propose an event aggregation mechanism to effectively integrate historical knowledge into node representations. Finally, we conduct extensive experiments on four public datasets to evaluate and analyze EVP.