Enhancing Graph Neural Networks for Out-of-Distribution Graph Detection.

Graph neural networks (GNNs) have shown promise in graph classification tasks, but they struggle to identify out-of-distribution (OOD) graphs often encountered in real-world scenarios, posing a significant obstacle for their open-world deployment. Due to the unpredictable nature of the various distributions to which OOD graphs adhere, the challenge of OOD graph detection lies in enabling models to capture distribution differences between in-distribution (ID) and OOD graphs. Current methods often introduce a subset of OOD patterns, such as synthetic OOD graphs, to facilitate learning the discrimination between ID and OOD graphs. However, these OOD patterns may not sufficiently encapsulate the entire range of OOD graphs, leading to inadequate learning of the distribution differences between ID and OOD graphs. In this article, we propose a novel OOD graph detection algorithm, ODGNN. The ODGNN does not expose GNNs to any OOD patterns during model training, thus reducing bias toward specific types of OOD graph samples and enhancing OOD graph detection. The algorithm differentiates graphs by evaluating whether the input graphs conform to established ID graph class-conditioned distributions. Specifically, during model training, the ODGNN integrates a Gaussian encoder into GNNs to characterize ID graph classes using distinct class-conditioned distributions. During inference, OOD graphs are mapped to a representation space distant from ID graphs due to their divergence from any known class-conditioned distribution. Extensive experiments conducted on real-world datasets validate the effectiveness of the ODGNN in enhancing OOD detection performance across various GNN-based graph classification models. The ODGNN also demonstrates superior performance compared to state-of-the-art OOD graph detection competitors.