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Due to inappropriate sample selection and limited training data, a
distribution shift often exists between the training and test sets. This shift
can adversely affect the test performance of Graph Neural Networks (GNNs).
Existing approaches mitigate this issue by either enhancing the robustness of
GNNs to distribution shift or reducing the shift itself. However, both
approaches necessitate retraining the model, which becomes unfeasible when the
model structure and parameters are inaccessible. To address this challenge, we
propose FR-GNN, a general framework for GNNs to conduct feature reconstruction.
FRGNN constructs a mapping relationship between the output and input of a
well-trained GNN to obtain class representative embeddings and then uses these
embeddings to reconstruct the features of labeled nodes. These reconstructed
features are then incorporated into the message passing mechanism of GNNs to
influence the predictions of unlabeled nodes at test time. Notably, the
reconstructed node features can be directly utilized for testing the
well-trained model, effectively reducing the distribution shift and leading to
improved test performance. This remarkable achievement is attained without any
modifications to the model structure or parameters. We provide theoretical
guarantees for the effectiveness of our framework. Furthermore, we conduct
comprehensive experiments on various public datasets. The experimental results
demonstrate the superior performance of FRGNN in comparison to multiple
categories of baseline methods.