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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
SynGraphy: Succinct Summarisation of Large Networks via Small Synthetic Representative Graphs—0
MD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction—0
Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph MatchingCode1
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph GenerationCode1
Dynamic Sensor Placement Based on Sampling Theory for Graph Signals—0
Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying GlassCode0
Efficient Directed Graph Sampling via Gershgorin Disc Alignment—0
Graph sampling for node embedding—0
Joint Network Topology Inference via a Shared Graphon ModelCode0
Sampling for network function learning—0
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