Graph neural networks (GNNs) have become increasingly popular in modeling graph-structured data due to their ability to learn node representations by aggregating local structure information. However, it is widely acknowledged that the test graph structure may differ from the training graph structure, resulting in a structure shift. In this paper, we experimentally find that the performance of GNNs drops significantly when the structure shift happens, suggesting that the learned models may be biased towards specific structure patterns. To address this challenge, we propose the Cluster Information Transfer (CIT) mechanism (Code available at https://github.com/BUPT-GAMMA/CITGNN), which can learn invariant representations for GNNs, thereby improving their generalization ability to various and unknown test graphs with structure shift. The CIT mechanism achieves this by combining different cluster information with the nodes while preserving their cluster-independent information. By generating nodes across different clusters, the mechanism significantly enhances the diversity of the nodes and helps GNNs learn the invariant representations. We provide a theoretical analysis of the CIT mechanism, showing that the impact of changing clusters during structure shift can be mitigated after transfer. Additionally, the proposed mechanism is a plug-in that can be easily used to improve existing GNNs. We comprehensively evaluate our proposed method on three typical structure shift scenarios, demonstrating its effectiveness in enhancing GNNs' performance.

翻译：图神经网络（GNN）因能够通过聚合局部结构信息学习节点表示，在建模图结构数据中日益流行。然而，普遍认为测试图结构与训练图结构可能存在差异，导致结构偏移现象。本文通过实验发现，当发生结构偏移时，GNN的性能显著下降，表明所学模型可能对特定结构模式存在偏差。为解决该挑战，我们提出聚类信息迁移（CIT）机制（代码开源于https://github.com/BUPT-GAMMA/CITGNN），该机制能学习GNN的不变表示，从而提升其对存在结构偏移的各种未知测试图的泛化能力。CIT机制通过将不同聚类信息与节点结合，同时保留其聚类无关信息来实现这一目标。通过跨聚类生成节点，该机制显著增强了节点多样性，并帮助GNN学习不变表示。我们对CIT机制进行了理论分析，表明结构偏移过程中聚类变化的影响可在迁移后得到缓解。此外，该机制作为即插即用模块，可便捷地用于改进现有GNN。我们在三种典型结构偏移场景下全面评估了所提方法，证明了其在提升GNN性能方面的有效性。