Title: N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification,
Abstract: 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.