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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 71–80 of 101 papers

TitleStatusHype
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
Dynamic Sensor Placement Based on Sampling Theory for Graph Signals—0
Edge Sampling of Graphs: Graph Signal Processing Approach With Edge Smoothness—0
Efficient Directed Graph Sampling via Gershgorin Disc Alignment—0
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment—0
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction—0
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