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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 61–70 of 101 papers

TitleStatusHype
Graph sampling for node embedding—0
Joint Network Topology Inference via a Shared Graphon ModelCode0
Sampling for network function learning—0
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
Neuro-Symbolic Procedural Planning with Commonsense Prompting—0
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks—0
Learning Large-scale Network Embedding from Representative Subgraph—0
Learning Dynamic Preference Structure Embedding From Temporal NetworksCode0
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