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gasteigerjo / ppnp

PPNP & APPNP models from "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019)
https://www.daml.in.tum.de/ppnp
MIT License
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How do you tune the hyper-paramerters? #8

Closed JinYang88 closed 4 years ago

JinYang88 commented 4 years ago

I was wondering if my understanding is correct:

You actually create 4 datasets, train, early-stopping, val, test. Then, you tune hyper-parameters on [ train, early-stopping, val], after the best param is obtained by choosing the best-performing one on val set, the param is then applied on the test set (the 4th set) to report the accuracy.

Thanks!

gasteigerjo commented 4 years ago

Almost. Additionally, we first split the data into a visible (development) set and a test set. The train, early-stopping and validation sets are drawn from the visible set with varying seeds. Evaluating the model on more than one training data split is essential for GNNs. See Appendix C in the paper for an exact description and illustration.