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Graph Neural Networks (GNNs) are de facto node classification models in graphstructured data. However, during testing-time, these algorithms assume no datashift, i.e., $\Pr_\text{train}(X,Y) = \Pr_\text{test}(X,Y)$. Domain adaptionmethods can be adopted for data shift, yet most of them are designed to onlyencourage similar feature distribution between source and target data.Conditional shift on classes can still affect such adaption. Fortunately, graphyields graph homophily across different data distributions. In response, wepropose Shift-Robust Node Classification (SRNC) to address these limitations.We introduce an unsupervised cluster GNN on target graph to group the similarnodes by graph homophily. An adversarial loss with label information on sourcegraph is used upon clustering objective. Then a shift-robust classifier isoptimized on training graph and adversarial samples on target graph, which aregenerated by cluster GNN. We conduct experiments on both open-set shift andrepresentation-shift, which demonstrates the superior accuracy of SRNC ongeneralizing to test graph with data shift. SRNC is consistently better thanprevious SoTA domain adaption algorithm on graph that progressively use modelpredictions on target graph for training.

Shift-Robust Node Classification via Graph Adversarial Clustering