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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 1–25 of 101 papers

TitleStatusHype
TF-GNN: Graph Neural Networks in TensorFlowCode3
Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph ReasoningCode3
Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGsCode2
Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload AwarenessCode2
Training Graph Neural Networks with 1000 LayersCode2
Scalable Graph Neural Networks via Bidirectional PropagationCode1
Neighborhood Matching Network for Entity AlignmentCode1
Retrofitting Temporal Graph Neural Networks with TransformerCode1
GraphSAINT: Graph Sampling Based Inductive Learning MethodCode1
Efficient Graph Deep Learning in TensorFlow with tf_geometricCode1
Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage AccessesCode1
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
GIST: Distributed Training for Large-Scale Graph Convolutional NetworksCode1
SIGN: Scalable Inception Graph Neural NetworksCode1
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through GradientsCode1
GRAPES: Learning to Sample Graphs for Scalable Graph Neural NetworksCode1
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph MatchingCode1
Generalize a Small Pre-trained Model to Arbitrarily Large TSP InstancesCode1
Accurate, Efficient and Scalable Training of Graph Neural NetworksCode1
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph GenerationCode1
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate CommunicationCode1
Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNsCode1
Heterogeneous Graph TransformerCode1
HyFactor: Hydrogen-count labelled graph-based defactorization AutoencoderCode1
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
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