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