Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs
Journal:
arXiv
Published Date:
Feb 25, 2025
Abstract
Despite extensive research efforts focused on OOD detection on images, OOD
detection on nodes in graph learning remains underexplored. The dependence
among graph nodes hinders the trivial adaptation of existing approaches on
images that assume inputs to be i.i.d. sampled, since many unique features and
challenges specific to graphs are not considered, such as the heterophily
issue. Recently, GNNSafe, which considers node dependence, adapted energy-based
detection to the graph domain with state-of-the-art performance, however, it
has two serious issues: 1) it derives node energy from classification logits
without specifically tailored training for modeling data distribution, making
it less effective at recognizing OOD data; 2) it highly relies on energy
propagation, which is based on homophily assumption and will cause significant
performance degradation on heterophilic graphs, where the node tends to have
dissimilar distribution with its neighbors. To address the above issues, we
suggest training EBMs by MLE to enhance data distribution modeling and remove
energy propagation to overcome the heterophily issues. However, training EBMs
via MLE requires performing MCMC sampling on both node feature and node
neighbors, which is challenging due to the node interdependence and discrete
graph topology. To tackle the sampling challenge, we introduce DeGEM, which
decomposes the learning process into two parts: a graph encoder that leverages
topology information for node representations and an energy head that operates
in latent space. Extensive experiments validate that DeGEM, without OOD
exposure during training, surpasses previous state-of-the-art methods,
achieving an average AUROC improvement of 6.71% on homophilic graphs and 20.29%
on heterophilic graphs, and even outperform methods trained with OOD exposure.
Our code is available at: https://github.com/draym28/DeGEM.
