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Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
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Classifier and exemplar synthesis for zero-shot learning
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FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
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Deep graph kernels
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Deep networks with stochastic depth
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Revisiting semi-supervised learning with graph embeddings
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Learning graph representations with embedding propagation
Duran, A. G. and Niepert, M · 2017
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Li, Q., Han, Z., and Wu, X · 2018
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Semi-supervised user geolocation via graph convolutional networks
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Attention-based graph neural network for semi-supervised learning
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Graph Attention Networks
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Zero-shot recognition via semantic embeddings and knowledge graphs
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Gaan: Gated attention networks for learning on large and spatiotemporal graphs
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Graph Neural Networks: A Review of Methods and Applications
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Predict then propagate: Graph neural networks meet personalized pagerank
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Label efficient semi-supervised learning via graph filtering
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Lanczosnet: Multi-scale deep graph convolutional networks
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Deep Graph InfoMax
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A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S · 2019
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How powerful are graph neural networks?
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Graph convolutional networks for text classification
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