Comments (6)
from pygcn.
from pygcn.
Hi,
I want to know which version is better in the classification.
from pygcn.
from pygcn.
Well, I think in the function normalize(mx) it normalizes the features by row,
but if the node features are not one-hot form, and each node has its own feature
like node1 : 1,3,7,20,57, which each feature represents different information( just like something similar in traditional machine learning, the different features has different meaning, like age, height, etc)
the each column of features has different dimension,
should I normalize them by column, instead of row??
from pygcn.
THX,I'll have a try
from pygcn.
Related Issues (20)
- specifying modes train, validation and test HOT 1
- Where is Filter parameters in the code? HOT 1
- Hi, does pandas make the data preprocessing more simple?
- Normalization of features, batch-wise training, feature extraction
- question about the adjacency matrix HOT 2
- citeseer dataset seems doesnot work HOT 2
- Difference between TF and Pytorch version code HOT 5
- In tensor flow code you used early stopping,isn't it needed in pytorch???
- In `utils.py` line 36, wouldnt `adj = adj + (adj.T > adj)` also work? HOT 2
- Invoice node classification / meta-data extraction / single prediction with trained model
- How to do a semi-supervised learning? HOT 6
- Predicting node degree
- Question About fastmode
- Error: 'pybind11' must be installed before running the build.
- transform to other scope dataset
- Why do row normalization instead of column normalization? HOT 2
- About the dataset split HOT 3
- citeseer dataset
- Cora dataset attributes
- accuracy in the experimental results HOT 1
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