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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

Showing 5175 of 101 papers

TitleStatusHype
Simple yet Effective Graph Distillation via Clustering0
STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation0
SynGraphy: Succinct Summarisation of Large Networks via Small Synthetic Representative Graphs0
Text Graph Transformer for Document Classification0
The Limits of Graph Samplers for Training Inductive Recommender Systems: Extended results0
Towards joint graph learning and sampling set selection from data0
Unsupervised Learning of Morphology with Graph Sampling0
Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data0
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies0
Adaptive Least Mean Squares Estimation of Graph Signals0
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs0
A Sampling Theory Perspective of Graph-based Semi-supervised Learning0
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG0
Bearing Fault Diagnosis using Graph Sampling and Aggregation Network0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs0
BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch0
Classification of developmental and brain disorders via graph convolutional aggregation0
Counting Motifs with Graph Sampling0
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training0
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs0
Dynamic Sensor Placement Based on Sampling Theory for Graph Signals0
Edge Sampling of Graphs: Graph Signal Processing Approach With Edge Smoothness0
Efficient Directed Graph Sampling via Gershgorin Disc Alignment0
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment0
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction0
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