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.