Closed junkangwu closed 4 years ago
Hi there, thanks for your attention. For the feature smoothness, the definition is derived from graph signal processing. You can check with PyGSP.
As for your code, I think it's alright. But note that the feature vectors of cora are not normalized. In the paper I mentioned that the feature space should be [0,1]^{d_k}, which means you need to normalize it first.
from sklearn import preprocessing
min_max_scaler = preprocessing.MinMaxScaler()
features= min_max_scaler.fit_transform(features)
Then I think you'll get the right anwser!
oh, I understand it ! Thanks so much!! I ignore it before... Thanks for your patience!
Great! I'll close the issue.
Nice to meet you, I am interesting in your paper in 2020ICLR-"MEASURING AND IMPROVING THE USE OF GRAPH INFORMATION IN GRAPH NEURAL NETWORKS".But when I plan to caculate some feature smoothness,I found it has some difference.I wish you could help me out! To caculate the feature smoothness of dataset --cora, I use the folloing codes:
But the result of my codes is so big.I'm little confused ! THANKS A LOT!