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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 61–70 of 101 papers

TitleStatusHype
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs—0
A Sampling Theory Perspective of Graph-based Semi-supervised Learning—0
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG—0
Bearing Fault Diagnosis using Graph Sampling and Aggregation Network—0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch—0
Classification of developmental and brain disorders via graph convolutional aggregation—0
Counting Motifs with Graph Sampling—0
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training—0
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs—0
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