COLLAB - Githubissues

weihua916 / powerful-gnns

How Powerful are Graph Neural Networks?

MIT License

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COLLAB #7

Open xinhandi opened 5 years ago

xinhandi commented 5 years ago

Dear Author,

I could reproduce task including MYTAG,PTC,NCl1,PROTEINS,IMDB-B,IMDB-M,RDT-M5K using the parameters of your paper, however, I could not reproduce the results for RDT-B and COLLAB using the parameters.

Might I get your advice or suggestion about reproducing the results of COLLAB and RDT-B.

Regards

LYF14020510036 commented 5 years ago

hello, the code does not have a validation set, how to get the test set result.

xinhandi commented 5 years ago

I reviewer the closed topic and try the --degree_as_tag

mulinmeng commented 4 years ago

Dear Author,

I could reproduce task including MYTAG,PTC,NCl1,PROTEINS,IMDB-B,IMDB-M,RDT-M5K using the parameters of your paper, however, I could not reproduce the results for RDT-B and COLLAB using the parameters.

Might I get your advice or suggestion about reproducing the results of COLLAB and RDT-B.

Regards

Hi, I want to ask how do you reproduce the results? Like the MYTAG, I tried all the combinations of the paper's mentioned hyper-parameters......

Thx!!!!!!!!!!!!!

weihua916 commented 3 years ago

Two quick points you can confirm are:

Have you correctly specified --graph_pooling_type? We used the sum graph pooling for bio/chemistry datasets and the mean graph pooling for social network datasets.
Have you added --deg_as_tag (use the node degree as the input node feature) for IMDB and COLLAB?