Semi-Supervised Classification with Graph Convolutional Networks

The paper presents a scalable approach for semi-supervised learning on graph-structured data, built on an efficient variant of convolutional neural networks that operates directly on graphs rather than requiring separate feature engineering. The choice of convolutional architecture is motivated via a localized first-order approximation of spectral graph convolutions, giving a model that scales linearly in the number of graph edges while learning hidden layer representations that encode both local graph structure and node features.

In experiments on citation networks and a knowledge graph dataset, the proposed approach outperforms related methods by a significant margin, demonstrating that this simplified, linearly-scaling graph convolution formulation is both computationally efficient and effective for semi-supervised node classification tasks on graph data.