GOODAT: Towards Test-time Graph Out-of-Distribution Detection
Luzhi Wang Dongxiao He He Zhang Yixin Liu Wenjie Wang Shirui Pan Di Jin Tat-Seng Chua
Graph neural networks (GNNs) have found widespread application in modeling
graph data across diverse domains. While GNNs excel in scenarios where the
testing data shares the distribution of their training counterparts (in
distribution, ID), they often exhibit incorrect predictions when confronted
with samples from an unfamiliar distribution (out-of-distribution, OOD). To
identify and reject OOD samples with GNNs, recent studies have explored graph
OOD detection, often focusing on training a specific model or modifying the
data on top of a well-trained GNN. Despite their effectiveness, these methods
come with heavy training resources and costs, as they need to optimize the
GNN-based models on training data. Moreover, their reliance on modifying the
original GNNs and accessing training data further restricts their universality.
To this end, this paper introduces a method to detect Graph Out-of-Distribution
At Test-time (namely GOODAT), a data-centric, unsupervised, and plug-and-play
solution that operates independently of training data and modifications of GNN
architecture. With a lightweight graph masker, GOODAT can learn informative
subgraphs from test samples, enabling the capture of distinct graph patterns
between OOD and ID samples. To optimize the graph masker, we meticulously
design three unsupervised objective functions based on the graph information
bottleneck principle, motivating the masker to capture compact yet informative
subgraphs for OOD detection. Comprehensive evaluations confirm that our GOODAT
method outperforms state-of-the-art benchmarks across a variety of real-world
datasets. The code is available at Github: https://github.com/Ee1s/GOODAT