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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 21–30 of 101 papers

TitleStatusHype
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training—0
A Sampling-based Framework for Hypothesis Testing on Large Attributed GraphsCode0
Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph ReasoningCode3
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs—0
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs—0
Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models—0
Classification of developmental and brain disorders via graph convolutional aggregation—0
Cooperative Minibatching in Graph Neural NetworksCode0
Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGsCode2
GRAPES: Learning to Sample Graphs for Scalable Graph Neural NetworksCode1
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