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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 11–20 of 101 papers

TitleStatusHype
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate CommunicationCode1
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph MatchingCode1
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph GenerationCode1
HyFactor: Hydrogen-count labelled graph-based defactorization AutoencoderCode1
GIST: Distributed Training for Large-Scale Graph Convolutional NetworksCode1
Efficient Graph Deep Learning in TensorFlow with tf_geometricCode1
Generalize a Small Pre-trained Model to Arbitrarily Large TSP InstancesCode1
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through GradientsCode1
Scalable Graph Neural Networks via Bidirectional PropagationCode1
Accurate, Efficient and Scalable Training of Graph Neural NetworksCode1
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