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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 41–50 of 101 papers

TitleStatusHype
Efficient Directed Graph Sampling via Gershgorin Disc Alignment—0
Bearing Fault Diagnosis using Graph Sampling and Aggregation Network—0
Edge Sampling of Graphs: Graph Signal Processing Approach With Edge Smoothness—0
Dynamic Sensor Placement Based on Sampling Theory for Graph Signals—0
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs—0
GraphBGS: Background Subtraction via Recovery of Graph Signals—0
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training—0
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG—0
Adaptive Least Mean Squares Estimation of Graph Signals—0
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better—0
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