GraphCL
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Shen-Lab
install the enviroment failed
pip install torch-scatter==1.1.0 -f https://pytorch-geometric.com/whl/torch-1.4.0.html pip install torch-sparse==0.4.4 -f https://pytorch-geometric.com/whl/torch-1.4.0.html pip install torch-cluster==1.4.5 -f https://pytorch-geometric.com/whl/torch-1.4.0.html pip install torch-spline-conv==1.1.0 -f https://pytorch-geometric.com/whl/torch-1.4.0.html pip install torch-vision==0.5.0
I am trying to install these but all failed, I am asking for help if there is someone who successfully installed these pakeges? Thank you.
Hi @Austinzhenghua,
Thanks for your interest. For semi_TU experiments, if you fail to work with torch_geometric==1.4.0 which is out-of-dated, you can take a try an upgraded version https://github.com/Shen-Lab/GraphCL_Automated/tree/master/semisupervised_TU that should work on torch_geometric >= 1.6.0.
Hi, I have a question about the dataset. If I have my own data but not graph data. Can I use KNN to construct some r ego network to instead the TU dataset? Thank you so much! hua zheng @.*** 签名由 网易邮箱大师 定制 On 06/29/2021 21:41, Yuning You wrote: Hi @Austinzhenghua, Thanks for your interest. For semi_TU experiments, if you fail to work with torch_geometric==1.4.0 which is out-of-dated, you can take a try an upgraded version https://github.com/Shen-Lab/GraphCL_Automated/tree/master/semisupervised_TU that should work on torch_geometric >= 1.6.0. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub, or unsubscribe.
I use KNN for each data point to construct N subgraph or just use one time to construct a graph? hua zheng @.*** 签名由 网易邮箱大师 定制 On 06/30/2021 02:07, Yuning You wrote: Yes, you can construct ur dataset by KNN. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub, or unsubscribe.
So I should construct the graph data same format like the TU dataset? the raw data have many documents as far as I know. Thank you for your kind help! hua zheng @.*** 签名由 网易邮箱大师 定制 On 06/30/2021 09:28, Yuning You wrote: Looks like either work for me. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub, or unsubscribe.