Hello author, sorry about a stupid question. But the Cora dataset has Cora.cites corresponding your cora_edges.csv, and Cora.content's paper index and paper category for your cora_target.csv, so I don't understand the meanning of your cora_features.json . In the beginning, I just think it's an adjacency matrix of all nodes(paper index), however, the content are inconsistent. Such as ,in cora_edges.csv it's as the picture as follw: and in cora_features.json it's : So I am confused , and hope for your answer. Thank you very much.

Hi @benedekrozemberczki ,

Let me first thank you for this promising work.

I would like to apply your GWNN to graph classification problems rather than nodes classification.

Do you have any extension for that ?

Thank you

Hi, author， There was a variable in the code called diagnoal_weight_filter I think the variable should change in the trainning time,but it never changed when I debugging. It's so confusing. And I wonder if the variable conduct the same role as the diagnoal_weight_filer in the tensorflow implementation will change.

hi, author. These days i've been watching the program. But when I run on this code, I find an error happened during the time. Can you give me some suggestions?

Hello~, Thank you for your paper. when I read the paper, I think about what is the connection between wavelet basis and Fourier basis, can you give me some tips?

Hello, I was running the example and got this error.

python src/main.py
+---------------------+----------------------------+
|      Parameter      |           Value            |
+=====================+============================+
| Approximation order | 20                         |
+---------------------+----------------------------+
| Dropout             | 0.500                      |
+---------------------+----------------------------+
| Edge path           | ./input/cora_edges.csv     |
+---------------------+----------------------------+
| Epochs              | 300                        |
+---------------------+----------------------------+
| Features path       | ./input/cora_features.json |
+---------------------+----------------------------+
| Filters             | 16                         |
+---------------------+----------------------------+
| Learning rate       | 0.001                      |
+---------------------+----------------------------+
| Log path            | ./logs/cora_logs.json      |
+---------------------+----------------------------+
| Scale               | 1                          |
+---------------------+----------------------------+
| Seed                | 42                         |
+---------------------+----------------------------+
| Target path         | ./input/cora_target.csv    |
+---------------------+----------------------------+
| Test size           | 0.200                      |
+---------------------+----------------------------+
| Tolerance           | 0.000                      |
+---------------------+----------------------------+
| Weight decay        | 0.001                      |
+---------------------+----------------------------+

Wavelet calculation and sparsification started.

100%|███████████████████████████████████████████████████████████████████████████████████| 2708/2708 [00:11<00:00, 237.23it/s]
100%|███████████████████████████████████████████████████████████████████████████████████| 2708/2708 [00:11<00:00, 228.91it/s]

Normalizing the sparsified wavelets.

Density of wavelets: 0.2%.
Density of inverse wavelets: 0.04%.

Training.

Loss:   0%|                                                                                          | 0/300 [00:00<?, ?it/s]Traceback (most recent call last):
  File "src/main.py", line 24, in <module>
    main()
  File "src/main.py", line 18, in main
    trainer.fit()
  File "/home/paperspace/Thesis/GraphWaveletNeuralNetwork/src/gwnn.py", line 131, in fit
    prediction = self.model(self.phi_indices, self.phi_values , self.phi_inverse_indices, self.phi_inverse_values, self.feature_indices, self.feature_values)
  File "/home/paperspace/miniconda2/envs/thesis/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/paperspace/Thesis/GraphWaveletNeuralNetwork/src/gwnn.py", line 44, in forward
    deep_features_1 = self.convolution_1(phi_indices, phi_values, phi_inverse_indices, phi_inverse_values, feature_indices, feature_values, self.args.dropout)
  File "/home/paperspace/miniconda2/envs/thesis/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/paperspace/Thesis/GraphWaveletNeuralNetwork/src/gwnn_layer.py", line 55, in forward
    localized_features = spmm(phi_product_indices, phi_product_values, self.ncount, filtered_features)
  File "/home/paperspace/miniconda2/envs/thesis/lib/python3.6/site-packages/torch_sparse/spmm.py", line 21, in spmm
    out = scatter_add(out, row, dim=0, dim_size=m)
  File "/home/paperspace/miniconda2/envs/thesis/lib/python3.6/site-packages/torch_scatter/add.py", line 73, in scatter_add
    return out.scatter_add_(dim, index, src)
RuntimeError: the derivative for 'index' is not implemented