Learning Invariant Representations of Graph Neural Networks via Cluster Generalization

arXiv:2403.03599v1 Announce Type: new
Abstract: 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 …

arxiv cluster cs.lg graph graph neural networks networks neural networks type via