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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
Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment—0
Federated Graph Learning with Adaptive Importance-based Sampling—0
FedGraph: Federated Graph Learning with Intelligent Sampling—0
Folded Graph Signals: Sensing with Unlimited Dynamic Range—0
GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis—0
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
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