  Recently, test time adaptation (TTA) has attracted increasing attention due
to its power of handling the distribution shift issue in the real world. Unlike
what has been developed for convolutional neural networks (CNNs) for image
data, TTA is less explored for Graph Neural Networks (GNNs). There is still a
lack of efficient algorithms tailored for graphs with irregular structures. In
this paper, we present a novel test time adaptation strategy named Graph
Adversarial Pseudo Group Contrast (GAPGC), for graph neural networks TTA, to
better adapt to the Out Of Distribution (OOD) test data. Specifically, GAPGC
employs a contrastive learning variant as a self-supervised task during TTA,
equipped with Adversarial Learnable Augmenter and Group Pseudo-Positive Samples
to enhance the relevance between the self-supervised task and the main task,
boosting the performance of the main task. Furthermore, we provide theoretical
evidence that GAPGC can extract minimal sufficient information for the main
task from information theory perspective. Extensive experiments on molecular
scaffold OOD dataset demonstrated that the proposed approach achieves
state-of-the-art performance on GNNs.
