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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
Graph sampling for node embedding—0
Folded Graph Signals: Sensing with Unlimited Dynamic Range—0
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs—0
Gophormer: Ego-Graph Transformer for Node Classification—0
FedGraph: Federated Graph Learning with Intelligent Sampling—0
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better—0
Classification of developmental and brain disorders via graph convolutional aggregation—0
Federated Graph Learning with Adaptive Importance-based Sampling—0
Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment—0
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs—0
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