Map to Optimal: Adapting Graph Out-of-Distribution in Test Time
Keywords: Out-of-distribution Generalizarion, Test-time Adaptation, Graph Neural Network, Self-supervision
Abstract: Based on topological proximity message passing, graph neural networks (GNNs) can quickly model data patterns on graphs. However, at test time, when the node feature and topological structure of the graph data are out-of-distribution (OOD), the performance of pre-trained GNNs will be hindered. Existing test-time methods either fine-tune the pre-trained model or overlook the discrepancy between the prior knowledge in pre-trained models and the test graph. We propose a novel self-supervised test-time adaptation paradigm GOAT (*https://anonymous.4open.science/r/GOAT-5C0E*), through graph augmentation-to-augmentation strategy, that enables a simple adapter can mitigate the distribution gap of training data and test-time data. GOAT reduces generalization error for node classification in various pre-trained settings through experiments on six benchmark datasets spanning three distinct real-world OOD scenarios. Remarkably, GOAT outperforms state-of-the-art test-time methods, and our empirical study further demonstrates the interpretability of the OOD representation generated from our method.
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 7480
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