DATE: Dual Prompt Learning with Information Bottleneck for Graph Out-of-Distribution Generalization

This paper studies the problem of graph out-of-distribution generalization, which aims to enhance the performance of graph neural networks (GNNs) under distribution shifts. Existing approaches usually learn graph representations from a casual graph, which may not explicitly utilize environment information explicitly. Furthermore, they could suffer from performance degradation when confusing semantics related to target labels and environments. In this paper, we propose a novel approach named Dual Prompt Learning with Information Bottleneck (DATE) for graph out-of-distribution generalization. The core of our DATE is to utilize dual prompts to extract task-oriented semantics and model distribution shifts, respectively. In particular, we first pre-train a GNN using contrastive learning with pretext tokens introduced. More importantly, we not only introduce a task-oriented prompt based on LLMs to generate environment-invariant representations, but also learn the environment-oriented prompts to simulate subgraphs in different environments. To optimize our prompts, we introduce a graph information bottleneck framework, which minimizes the mutual information between environment-invariant representations and environment semantics with the most semantics preserved. Extensive experiments on various benchmark datasets validate the effectiveness of our DATE against various state-of-the-art approaches.