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Training Graph Convolutional Networks (GCNs) is expensive as it needs to aggregate data recursively from neighboring nodes.
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Solving stochastic compositional optimization is nearly as easy as solving stochastic optimization
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Minimal variance sampling with provable guarantees for fast training of graph neural networks
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FastGCN: Fast learning with graph convolutional networks via importance sampling
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Graph convolutional neural networks for web-scale recommender systems
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Hierarchical graph representation learning with differentiable pooling
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Agl: A scalable system for industrial-purpose graph machine learning
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