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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
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
Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment—0
Federated Graph Learning with Adaptive Importance-based Sampling—0
FedGraph: Federated Graph Learning with Intelligent Sampling—0
Folded Graph Signals: Sensing with Unlimited Dynamic Range—0
GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis—0
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