planetoid
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kimiyoung
original datasets ?
Hello, could you please provide the original dataset before your preprocessing ? It seems it does not match the dataset in the following link https://linqs.soe.ucsc.edu/data. It seems your train/val/test splits are well chosen. Some labels are also different from the original dataset in https://linqs.soe.ucsc.edu/data. Do you have any ideas about this ? Thanks.
No. If you are working on something related to GCN, you probably need to just use the random splits version.
Hi, I am working on this but confused on dataset. There is no clear explanation how to convert original dataset for gcn. Can you provide raw data preprocessing code files? or instructions how to do that?
Thank you !
Hi, I have the same question too. Could you please provide me raw data preprocessing code files?
Hey, anybody has solved this problem. Could you share your code with me? Many thanks...
Hi ,I try to write the code to create the dateset like yours but seems somting wrong. I use the dataset X = x_train+x_val+x_test (L =L_train+L_val+L_test ) to create the graph(X.shape[0]*X.shape[0]) am I wrong? do you have any idea about this problem,if so could you tell me,thank you very much!
Hello, could you please provide the original dataset before your preprocessing ? It seems it does not match the dataset in the following link https://linqs.soe.ucsc.edu/data. It seems your train/val/test splits are well chosen. Some labels are also different from the original dataset in https://linqs.soe.ucsc.edu/data. Do you have any ideas about this ? Thanks.
@Chunpai I met the same issue when regenerating the data, cannot get such high performance by random selection. Have you solved your issue?
Hi, I have the same question too. Could you please provide me raw data preprocessing code files?
Hello, could you please provide the original dataset before your preprocessing ? It seems it does not match the dataset in the following link https://linqs.soe.ucsc.edu/data. It seems your train/val/test splits are well chosen. Some labels are also different from the original dataset in https://linqs.soe.ucsc.edu/data. Do you have any ideas about this ? Thanks.
@Chunpai I met the same issue when regenerating the data, cannot get such high performance by random selection. Have you solved your issue?
Excuse me, have you found the solution？
Hi, I am working on this but confused on dataset. There is no clear explanation how to convert original dataset for gcn. Can you provide raw data preprocessing code files? or instructions how to do that?
Thank you !
The original dataset (http://www.cs.umd.edu/~sen/lbc-proj/LBC.html) is processed using Pickle (https://github.com/NIRVANALAN/gcn_analysis/blob/master/notebook/Plantenoid%20Citation%20Data%20Format%20Transformation.ipynb).
