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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 61–70 of 101 papers

TitleStatusHype
Towards joint graph learning and sampling set selection from data—0
Unsupervised Learning of Morphology with Graph Sampling—0
Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data—0
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies—0
Adaptive Least Mean Squares Estimation of Graph Signals—0
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs—0
A Sampling Theory Perspective of Graph-based Semi-supervised Learning—0
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG—0
Bearing Fault Diagnosis using Graph Sampling and Aggregation Network—0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
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