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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 41–50 of 101 papers

TitleStatusHype
Empirical Risk Minimization and Stochastic Gradient Descent for Relational DataCode0
Accurate, Efficient and Scalable Graph EmbeddingCode0
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-IdentificationCode0
Learning Dynamic Preference Structure Embedding From Temporal NetworksCode0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
Efficient Directed Graph Sampling via Gershgorin Disc Alignment—0
Bearing Fault Diagnosis using Graph Sampling and Aggregation Network—0
Edge Sampling of Graphs: Graph Signal Processing Approach With Edge Smoothness—0
Dynamic Sensor Placement Based on Sampling Theory for Graph Signals—0
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs—0
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