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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 91–100 of 101 papers

TitleStatusHype
Impression-Informed Multi-Behavior Recommender System: A Hierarchical Graph Attention Approach—0
Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models—0
Keyed Chaotic Dynamics for Privacy-Preserving Neural Inference—0
Learning Large-scale Network Embedding from Representative Subgraph—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
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