  Graph Neural Networks (GNNs) have made tremendous progress in the graph
classification task. However, a performance gap between the training set and
the test set has often been noticed. To bridge such gap, in this work we
introduce the first test-time training framework for GNNs to enhance the model
generalization capacity for the graph classification task. In particular, we
design a novel test-time training strategy with self-supervised learning to
adjust the GNN model for each test graph sample. Experiments on the benchmark
datasets have demonstrated the effectiveness of the proposed framework,
especially when there are distribution shifts between training set and test
set. We have also conducted exploratory studies and theoretical analysis to
gain deeper understandings on the rationality of the design of the proposed
graph test time training framework (GT3).
