SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks (Extended Abstract)

Graph Convolutional Networks (GCNs) are powerful tools for learning representations in graph-structured data. However, their performance tends to degrade with increased model depth due to over-smoothing. Although previous studies attribute degradation to over-smoothing, this work identifies the mutually reinforcing effects of over-smoothing and gradient vanishing as the root cause. In this paper, we propose SkipNode, a plug-and-play module that mitigates degradation in deep GCNs. SkipNode introduces node-sampling in each convolutional layer to selectively skip convolutions, preventing over-smoothing by reducing the depth experienced by specific nodes and facilitating gradient backpropagation. We demonstrate both theoretically and experimentally that SkipNode effectively curtails over-smoothing and gradient vanishing, improving deep GCN performance across diverse tasks. Extensive evaluations show SkipNode's robustness and superior performance over state-of-the-art (SOTA) baselines, establishing it as a practical solution for training deep GCNs.