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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 76–100 of 101 papers

TitleStatusHype
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG—0
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-IdentificationCode0
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment—0
Pre-Training on Dynamic Graph Neural NetworksCode0
Learning Parameters for Balanced Index Influence Maximization—0
Text Graph Transformer for Document Classification—0
Accelerating Graph Sampling for Graph Machine Learning using GPUs—0
Little Ball of Fur: A Python Library for Graph SamplingCode0
Reducing Large Internet Topologies for Faster SimulationsCode0
On Random Walk Based Graph SamplingCode0
Folded Graph Signals: Sensing with Unlimited Dynamic Range—0
GraphBGS: Background Subtraction via Recovery of Graph Signals—0
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift—0
Influence maximization in unknown social networks: Learning Policies for Effective Graph SamplingCode0
Reconstruction-Cognizant Graph Sampling using Gershgorin Disc Alignment—0
Accurate, Efficient and Scalable Graph EmbeddingCode0
Empirical Risk Minimization and Stochastic Gradient Descent for Relational DataCode0
Counting Motifs with Graph Sampling—0
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies—0
Unsupervised Learning of Morphology with Graph Sampling—0
A Sampling Theory Perspective of Graph-based Semi-supervised Learning—0
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction—0
Graph sampling with determinantal processes—0
Adaptive Least Mean Squares Estimation of Graph Signals—0
Evaluating Visual Properties via Robust HodgeRank—0
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