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arXiv · 1802.08888

N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification

Sami Abu-El-Haija·Amol Kapoor·Bryan Perozzi·Joonseok Lee
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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.

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Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, Joonseok Lee. 2018-02-24. N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. https://arxiv.org/abs/1802.08888

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