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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
Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNsCode1
GraphSAINT: Graph Sampling Based Inductive Learning MethodCode1
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate CommunicationCode1
HyFactor: Hydrogen-count labelled graph-based defactorization AutoencoderCode1
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
Efficient Directed Graph Sampling via Gershgorin Disc Alignment—0
Bearing Fault Diagnosis using Graph Sampling and Aggregation Network—0
Adaptive Least Mean Squares Estimation of Graph Signals—0
FedGraph: Federated Graph Learning with Intelligent Sampling—0
Edge Sampling of Graphs: Graph Signal Processing Approach With Edge Smoothness—0
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