Research map / Graph learning / Graph algorithms and clustering
Unveiling the sampling density in non-uniform geometric graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a distance+hubs decoder instead of inner-product or MLP decoders | Graph autoencoder · Real-world link prediction datasets | AUC | Helped |
| Use a distance+hubs decoder instead of a constant-radius distance decoder | Graph autoencoder · Real-world link prediction datasets | AUC | Helped |
| Learn sampling density and use it to correct the GSO | Spectral graph neural network · Real-world node classification graphs | accuracy | Helped |
| Use degree-normalized GSOs while ignoring sampling density | Spectral graph neural network · Real-world node classification graphs | accuracy | Helped |
| Use learned inverse density to correct the GSO | Spectral graph neural network · AIDS | classification performance | Helped |
| Use learned inverse density for weighted pooling | Graph classification network · AIDS | classification performance | Helped |
| Replace uniform pooling with learned inverse-density weighted pooling | Graph classification network · AIDS | classification performance | Helped |
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