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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 21–30 of 101 papers

TitleStatusHype
Neighborhood Matching Network for Entity AlignmentCode1
SIGN: Scalable Inception Graph Neural NetworksCode1
Heterogeneous Graph TransformerCode1
GraphSAINT: Graph Sampling Based Inductive Learning MethodCode1
Keyed Chaotic Dynamics for Privacy-Preserving Neural Inference—0
Simple yet Effective Graph Distillation via Clustering—0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
The Limits of Graph Samplers for Training Inductive Recommender Systems: Extended results—0
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs—0
Hierarchical graph sampling based minibatch learning with chain preservation and variance reductionCode0
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