How can I do graph regression with graphormer? about graphormer

[zhengsx]

Hi, 9 is the number of node features (since there are 9 atom features for sample in pcqm4m dataset), and 512 is the maximum categories for each kind of feature. It's equivalent to seperately define 9 nn.Embedding(512, hidden_dim) for each feature. Hope this could address your question about the atom_encoder.

For another question about regression task, do you mean your label is a real value and the objective of model is to regress it? If so, you don't need to modify anything about the task. But if you mean that your feature is in continous space but not category feature, you could simply replace the nn.Embedding() from atom_encoder to a nn.Linear() or MLP.

[Sangyoon-Bae]

yes graph label is real value and objective of model is to regress it! then my node feature and edge feature are in continuous space. I'll try nn.Linear() ! I have another question, can I st edge feature and node feature as float type?

[zhengsx]

I think you can have a try, by the way, discretize the numerical features then using nn.Embedding() is also commonly used.

[Sangyoon-Bae]

Thanks for kind reply! Sorry for asking lots of question, I'd like to ask that should I change atom_encoder (line 63) , edge_encoder (line 64), out_proj (line 84), and downstream_out_proj (line 86) for regression task like pcqm4m-lsc?

[zhengsx]

If you want to use well-trained Graphormer (pre-trained on PCQM4M), you need to modify and use downstream_out_proj, otherwise you can use out_proj without modification. Whether atom_encoder and edge_encoder are needed to be modifed depends on your features as described in previous reply.

[Sangyoon-Bae]

Thanks a lot!! Your comment really helped me to train my model!!