🤖 AI Summary
To address the under-smoothing and over-smoothing issues inherent in Graph Convolutional Networks (GCNs) for semi-supervised learning on sparsely labeled graphs, this paper proposes GND-Nets: a single-layer graph neural network architecture. Its core innovation lies in introducing a learnable neural diffusion mechanism—embedding neural modules into linear or nonlinear graph diffusion processes to jointly model local neighborhood structures and global topological information. By integrating differentiable graph propagation with localized and global neighborhood aggregation, GND-Nets achieves a favorable trade-off between expressive power and training stability. Extensive experiments on multiple sparsely labeled graph benchmarks demonstrate that GND-Nets significantly outperforms state-of-the-art methods in node classification accuracy while exhibiting faster convergence.
📝 Abstract
Graph Convolutional Networks (GCN) is a pioneering model for graph-based semi-supervised learning. However, GCN does not perform well on sparsely-labeled graphs. Its two-layer version cannot effectively propagate the label information to the whole graph structure (i.e., the under-smoothing problem) while its deep version over-smoothens and is hard to train (i.e., the over-smoothing problem). To solve these two issues, we propose a new graph neural network called GND-Nets (for Graph Neural Diffusion Networks) that exploits the local and global neighborhood information of a vertex in a single layer. Exploiting the shallow network mitigates the over-smoothing problem while exploiting the local and global neighborhood information mitigates the under-smoothing problem. The utilization of the local and global neighborhood information of a vertex is achieved by a new graph diffusion method called neural diffusions, which integrate neural networks into the conventional linear and nonlinear graph diffusions. The adoption of neural networks makes neural diffusions adaptable to different datasets. Extensive experiments on various sparsely-labeled graphs verify the effectiveness and efficiency of GND-Nets compared to state-of-the-art approaches.
