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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
Scalable Graph Neural Networks via Bidirectional PropagationCode1
Accurate, Efficient and Scalable Training of Graph Neural NetworksCode1
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
Neighborhood Matching Network for Entity AlignmentCode1
SIGN: Scalable Inception Graph Neural NetworksCode1
Heterogeneous Graph TransformerCode1
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
GraphSAINT: Graph Sampling Based Inductive Learning MethodCode1
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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