Comments (3)
@zhuhm1996 Could you share your hyper-param for row 2, especially for RDT-B and RDT-M5K. We can only achieve 77 in RDT-B and 49 in RDT-M5K.
Thanks!
from powerful-gnns.
@zhuhm1996 I think there is a typo in the table. The first five columns are social network datasets and the latter four columns are bioinformatics datasets. In the "EXPERIMENTS" section of the paper it's stated that different hidden units are searched for the two different categories.
from powerful-gnns.
Hi, apologies for the delayed response. Two points that are often missed 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?
Besides, those datasets are outdated and are too small to rigorously compare different models. I'd suggest working on more interesting and modern graph datasets like https://ogb.stanford.edu .
from powerful-gnns.
Related Issues (20)
- dataset
- dataset HOT 2
- Data preprocessing HOT 2
- Apply GIN to node classification HOT 5
- 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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from powerful-gnns.