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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 51–60 of 101 papers

TitleStatusHype
Reducing Large Internet Topologies for Faster Simulations—0
Evaluating Visual Properties via Robust HodgeRank—0
Sampling for network function learning—0
Sampling From Large Graphs—0
Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery—0
Simple yet Effective Graph Distillation via Clustering—0
STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation—0
SynGraphy: Succinct Summarisation of Large Networks via Small Synthetic Representative Graphs—0
Text Graph Transformer for Document Classification—0
The Limits of Graph Samplers for Training Inductive Recommender Systems: Extended results—0
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