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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
GDM: Dual Mixup for Graph Classification with Limited Supervision—0
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs—0
Gophormer: Ego-Graph Transformer for Node Classification—0
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better—0
GraphBGS: Background Subtraction via Recovery of Graph Signals—0
Graph Neural Network for Stress Predictions in Stiffened Panels Under Uniform Loading—0
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift—0
Graph sampling for node embedding—0
Graph Sampling for Scalable and Expressive Graph Neural Networks on Homophilic Graphs—0
Graph sampling with determinantal processes—0
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