Graph Neural Networks with Adaptive Residual Jiayuan Ding 1 Wei Jin
–Neural Information Processing Systems
Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks. In this work, we discover an interesting phenomenon that although residual connections in the message passing of GNNs help improve the performance, they immensely amplify GNNs' vulnerability against abnormal node features. This is undesirable because in real-world applications, node features in graphs could often be abnormal such as being naturally noisy or adversarially manipulated. We analyze possible reasons to understand this phenomenon and aim to design GNNs with stronger resilience to abnormal features.
Neural Information Processing Systems
Jan-24-2025, 23:06:16 GMT
- Country:
- North America > United States > Michigan (0.28)
- Industry:
- Information Technology (0.47)
- Technology: