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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 1–10 of 101 papers

TitleStatusHype
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
Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNsCode1
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
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better—0
BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch—0
Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks—0
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