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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 31–40 of 101 papers

TitleStatusHype
Dynamic Sensor Placement Based on Sampling Theory for Graph Signals—0
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG—0
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs—0
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training—0
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better—0
Counting Motifs with Graph Sampling—0
A Sampling Theory Perspective of Graph-based Semi-supervised Learning—0
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs—0
GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis—0
Gophormer: Ego-Graph Transformer for Node Classification—0
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