Graph Partner Neural Networks for Semi-Supervised Learning on Graphs
This addresses a key bottleneck in graph neural networks for researchers and practitioners in semi-supervised learning, though it is incremental as it builds on existing GCN methods.
The paper tackles the over-smoothing issue in deep Graph Convolutional Networks (GCNs) by proposing Graph Partner Neural Networks (GPNN), which incorporate a de-parameterized GCN and MLP with consistency and KL losses, achieving state-of-the-art performance on node classification tasks.
Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, link prediction, and graph classification. However, it is inevitable for deep GCNs to suffer from an over-smoothing issue that the representations of nodes will tend to be indistinguishable after repeated graph convolution operations. To address this problem, we propose the Graph Partner Neural Network (GPNN) which incorporates a de-parameterized GCN and a parameter-sharing MLP. We provide empirical and theoretical evidence to demonstrate the effectiveness of the proposed MLP partner on tackling over-smoothing while benefiting from appropriate smoothness. To further tackle over-smoothing and regulate the learning process, we introduce a well-designed consistency contrastive loss and KL divergence loss. Besides, we present a graph enhancement technique to improve the overall quality of edges in graphs. While most GCNs can work with shallow architecture only, GPNN can obtain better results through increasing model depth. Experiments on various node classification tasks have demonstrated the state-of-the-art performance of GPNN. Meanwhile, extensive ablation studies are conducted to investigate the contributions of each component in tackling over-smoothing and improving performance.