Comments (5)
Hi, thanks for your interest. That's not surprising because we do not need expressive power in those node classification datasets. GIN is most useful when we really need expressive power (e.g., graph classification, or node classification with non-rich node features.)
from powerful-gnns.
Hi and thanks for sharing your code.
When applying GIN to node classification task for example on cora dataset, the accuracy is low.
You said in the paper that for mean aggregation and linear function GIN is GCN. I use the DGL implementation of GIN for node classification but I can't produce accuracy near to GCN.
IS there a need for some preprocessing when applying GIN on node classification?
Would you please share the accuracy of your node classification on the cora dataset?
from powerful-gnns.
would you please tell me how to use the GIN on cora,I don't know how to load the data.
from powerful-gnns.
from powerful-gnns.
Hi! Please use GIN and Cora in PyG.
from powerful-gnns.
Related Issues (20)
- dataset
- dataset HOT 2
- Data preprocessing HOT 2
- Reproduce Issues HOT 3
- Problem.
- Dropout in last layer HOT 2
- Inconsistent dataset description and actual data HOT 1
- What is the meaning of the phrase "perform 10-fold cross-validation with LIB-SVM" in the paper? HOT 2
- Low accuracy HOT 6
- Cannot reproduce result on COLLAB! HOT 2
- About node smoothing HOT 1
- GIN's discriminative power for directed graph
- Ask for information of discrete labels
- About node attributes
- Think about graph spectral
- Custom dataset creation HOT 4
- COLLAB HOT 4
- result of paper HOT 3
- Possible bug in `load_data()` HOT 1
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