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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 51–60 of 101 papers

TitleStatusHype
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
GDM: Dual Mixup for Graph Classification with Limited Supervision—0
Learning Parameters for Balanced Index Influence Maximization—0
Light-weight End-to-End Graph Interest Network for CTR Prediction in E-commerce Search—0
MD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction—0
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks—0
Neuro-Symbolic Procedural Planning with Commonsense Prompting—0
Accelerating Graph Sampling for Graph Machine Learning using GPUs—0
Node Copying: A Random Graph Model for Effective Graph Sampling—0
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