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Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning
Graph convolutional network (GCN) provides a powerful means for graph-ba...
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Connecting Graph Convolutional Networks and Graph-Regularized PCA
Graph convolution operator of the GCN model is originally motivated from...
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Semi-supervised User Geolocation via Graph Convolutional Networks
Social media user geolocation is vital to many applications such as even...
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Exploring Graph Learning for Semi-Supervised Classification Beyond Euclidean Data
Semi-supervised classification on graph-structured data has received inc...
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LFGCN: Levitating over Graphs with Levy Flights
Due to high utility in many applications, from social networks to blockc...
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Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification
The graph convolution network (GCN) is a widely-used facility to realize...
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AM-GCN: Adaptive Multi-channel Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have gained great popularity in tack...
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N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification
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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