N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification
This work addresses a domain-specific problem in graph machine learning, offering incremental improvements for researchers and practitioners in network analysis.
The paper tackles the problem of semi-supervised node classification on graph-structured data by proposing N-GCN, a model that combines GCNs with random walk information, resulting in improved state-of-the-art performance on datasets like Cora, Citeseer, Pubmed, and PPI.
Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper, we propose a model: Network of GCNs (N-GCN), which marries these two lines of work. At its core, N-GCN trains multiple instances of GCNs over node pairs discovered at different distances in random walks, and learns a combination of the instance outputs which optimizes the classification objective. Our experiments show that our proposed N-GCN model improves state-of-the-art baselines on all of the challenging node classification tasks we consider: Cora, Citeseer, Pubmed, and PPI. In addition, our proposed method has other desirable properties, including generalization to recently proposed semi-supervised learning methods such as GraphSAGE, allowing us to propose N-SAGE, and resilience to adversarial input perturbations.