Citeseer data set accuracy about pygcn HOT 18 OPEN

Note that this repository uses different dataset splits and a slightly different model architecture than in our original paper. For an exact replication of the experiments in our paper, please have a look at this repository: https://github.com/tkipf/gcn

from pygcn.

from pygcn.

from pygcn.

from pygcn.

from pygcn.

Hello. I also run the pytorch version GCN on citeseer dataset and the accuracy is 69.65%. Furthermore, the accuracy differed everytime on the cora dataset provided by this repository. However, the accuracy is invariable on datasets provided by the original GCN repository https://github.com/tkipf/gcn.
I can't understand why. I really want to know whether someone else got the same results.
Thank you.

Dear professor,
Hello!
I am very interesting in your recent GCN work.
Thanks for sharing the code, I used the GCN network to run the citeseer database, but the accuracy could not reach 70.3. How did you set the parameters to run so high? Thanks a lot for sharing the code, anyway.

Many thanks for your help.

from pygcn.

from pygcn.

@tkipf

Dear Thomas, if you have time could you elaborate just a bit on the "implementation changes" in the Pytorch version you mentioned above? I'm not necessarily interested in the cora data or those results but more interested in training on other graphs/datasets. Maybe even a parallel version at some point down the road using Horovod or Keras.

Thanks

from pygcn.

from pygcn.

@tkipf

Thanks Again!!

from pygcn.

One of the most important reason I think is that there is no available api in pytorch by which the dropout on sparse input can be implemented. In GCN-tensorflow-version Dr. Kipf implement sparse dropout by tf.sparse_retain but this api can not be found in torch. Because dropout is an important hyperparameterfor GCN (empirically) thus we may not have the ability to recover the accuracy without solving this problem.

Hello. I also run the pytorch version GCN on citeseer dataset and the accuracy is 69.65%. Furthermore, the accuracy differed everytime on the cora dataset provided by this repository. However, the accuracy is invariable on datasets provided by the original GCN repository https://github.com/tkipf/gcn.
I can't understand why. I really want to know whether someone else got the same results.
Thank you.

Dear professor,
Hello!
I am very interesting in your recent GCN work.
Thanks for sharing the code, I used the GCN network to run the citeseer database, but the accuracy could not reach 70.3. How did you set the parameters to run so high? Thanks a lot for sharing the code, anyway.
Many thanks for your help.

from pygcn.

from pygcn.

Dear professor,
Hello!
I am very interesting in your recent GCN work.
Thanks for sharing the code, I used the GCN network to run the citeseer database, but the accuracy could not reach 70.3. How did you set the parameters to run so high? Thanks a lot for sharing the code, anyway.

Many thanks for your help.

Hello, I want to know where can I download the citeseer dataset in the form similar to cora dataset in this implementation(citeseer.cites, citeseer.content).
Thank you a lot!

from pygcn.

Hello. I also run the pytorch version GCN on citeseer dataset and the accuracy is 69.65%. Furthermore, the accuracy differed everytime on the cora dataset provided by this repository. However, the accuracy is invariable on datasets provided by the original GCN repository https://github.com/tkipf/gcn.
I can't understand why. I really want to know whether someone else got the same results.
Thank you.

Dear professor,
Hello!
I am very interesting in your recent GCN work.
Thanks for sharing the code, I used the GCN network to run the citeseer database, but the accuracy could not reach 70.3. How did you set the parameters to run so high? Thanks a lot for sharing the code, anyway.
Many thanks for your help.

Hello!
Have you found the reason why the accuracy differed everytime on the cora dataset provided by this repository? If you know the reason, please tell me, thank you.

from pygcn.

Note that this repository uses different dataset splits and a slightly different model architecture than in our original paper. For an exact replication of the experiments in our paper, please have a look at this repository: https://github.com/tkipf/gcn

Dear Dr Kipf,

I am a big fan of your works and really interested in this code you shared.
Regarding Citeseer dataset, I have download it from https://github.com/kimiyoung/planetoid, which hopefully is the same data you ahve used.
My problem is reading this file and specifying which file creates graph and which one makes edges.
Can you please elaboarte it more.
Thank you in advance.

Cheers,

from pygcn.

Dear professor,
Hello!
I am very interesting in your recent GCN work.
Thanks for sharing the code, I used the GCN network to run the citeseer database, but the accuracy could not reach 70.3. How did you set the parameters to run so high? Thanks a lot for sharing the code, anyway.

Many thanks for your help.

Regarding Citeseer dataset, I have download it from https://github.com/kimiyoung/planetoid, which hopefully is the same data you ahve used.
My problem is reading this file and specifying which file creates graph and which one makes edges.
Can you please elaboarte it more.
Thank you in advance.

from pygcn.

Dear professor, Hello! It makes sense that you load the cora dataset this way and construct the adjacency matrix. idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset), dtype=np.dtype(str)) print(idx_features_labels.shape) features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32) labels = encode_onehot(idx_features_labels[:, -1]) # build graph idx = np.array(idx_features_labels[:, 0], dtype=np.int32) idx_map = {j: i for i, j in enumerate(idx)} edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset), dtype=np.int32) edges = np.array(list(map(idx_map.get, edges_unordered.flatten())), dtype=np.int32).reshape(edges_unordered.shape) adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(labels.shape[0], labels.shape[0]), dtype=np.float32) How do you load data for Cornell datasets in a WebKB dataset? The dataset can be seen in the attachment.I hope to get your help. Thank you very much!

Regarding Citeseer dataset, I have download it from https://github.com/kimiyoung/planetoid, which hopefully is the same data you ahve used.
My problem is reading this file and specifying which file creates graph and which one makes edges.
Can you please elaboarte it more.
Thank you in advance.

from pygcn.

Hello. I also run the pytorch version GCN on citeseer dataset and the accuracy is 69.65%. Furthermore, the accuracy differed everytime on the cora dataset provided by this repository. However, the accuracy is invariable on datasets provided by the original GCN repository https://github.com/tkipf/gcn.
I can't understand why. I really want to know whether someone else got the same results.
Thank you.

Dear professor,
Hello!
I am very interesting in your recent GCN work.
Thanks for sharing the code, I used the GCN network to run the citeseer database, but the accuracy could not reach 70.3. How did you set the parameters to run so high? Thanks a lot for sharing the code, anyway.
Many thanks for your help.

Hello!
Have you found the reason why the accuracy differed everytime on the cora dataset provided by this repository? If you know the reason, please tell me, thank you.

Regarding Citeseer dataset, I have download it from https://github.com/kimiyoung/planetoid, which hopefully is the same data you ahve used.
My problem is reading this file and specifying which file creates graph and which one makes edges.
Can you please elaboarte it more.
Thank you in advance.

from pygcn.