IBPL: Information Bottleneck-based Prompt Learning for graph out-of-distribution detection.

When training and test graph samples follow different data distributions, graph out-of-distribution (OOD) detection becomes an indispensable component of constructing the reliable and safe graph learning systems. Motivated by the significant progress on prompt learning, graph prompt-based methods, which enable a well-trained graph neural network to detect OOD graphs without modifying any model parameters, have been a standard benchmark with promising computational efficiency and model effectiveness. However, these methods ignore the influence of overlapping features existed in both in-distribution (ID) and OOD graphs, which weakens the difference between them and leads to sub-optimal detection results. In this paper, we present the Information Bottleneck-based Prompt Learning (IBPL) to overcome this challenging problem. Specifically, IBPL includes a new graph prompt that jointly performs the mask operation on node features and the graph structure. Building upon this, we develop an information bottleneck (IB)-based objective to optimize the proposed graph prompt. Since the overlapping features are inaccessible, IBPL introduces the noise data augmentation which generates a series of perturbed graphs to fully covering the overlapping features. Through minimizing the mutual information between the prompt graph and the perturbed graphs, our objective can eliminate the overlapping features effectively. In order to avoid the negative impact of perturbed graphs, IBPL simultaneously maximizes the mutual information between the prompt graph and the category label for better extracting the ID features. We conduct experiments on multiple real-world datasets in both supervised and unsupervised scenarios. The empirical results and extensive model analyses demonstrate the superior performance of IBPL over several competitive baselines.