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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 81–90 of 101 papers

TitleStatusHype
Learning Large-scale Network Embedding from Representative Subgraph—0
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-IdentificationCode0
Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying GlassCode0
Accurate, Efficient and Scalable Graph EmbeddingCode0
Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and RobustnessCode0
Sampling From Large GraphsCode0
Empirical Risk Minimization and Stochastic Gradient Descent for Relational DataCode0
Hierarchical graph sampling based minibatch learning with chain preservation and variance reductionCode0
Fine-tuning Partition-aware Item Similarities for Efficient and Scalable RecommendationCode0
On Random Walk Based Graph SamplingCode0
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