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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 51–60 of 101 papers

TitleStatusHype
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment—0
Node Copying: A Random Graph Model for Effective Graph Sampling—0
Fine-tuning Partition-aware Item Similarities for Efficient and Scalable RecommendationCode0
TF-GNN: Graph Neural Networks in TensorFlowCode3
Neuro-Symbolic Procedural Planning with Commonsense Prompting—0
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks—0
HyFactor: Hydrogen-count labelled graph-based defactorization AutoencoderCode1
Learning Large-scale Network Embedding from Representative Subgraph—0
Learning Dynamic Preference Structure Embedding From Temporal NetworksCode0
FedGraph: Federated Graph Learning with Intelligent Sampling—0
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