Comments (3)
I recommend using the data loader from https://github.com/tkipf/keras-gcn/blob/master/kegra/utils.py
Then you donβt have to deal with this strange .allx etc format (which is just supplied in this repo because it was used in a benchmark from an earlier paper from some other lab on which we base our evaluation on) :)
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i have the same question about how to apply it to my own dataset, so if you have solved this problem , could you give me some guidance please, thanks a lot !
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Dear @tkipf
first of all, thank you for the excellent work! Your paper and the provided code are helpful to get started with GCN.I'm currently trying to apply your algorithm to my data.
After looking at the load_data() function, I was able to create an adjacency matrix in the same format as your Cora example.However, I struggle with the node feature object, because I don't understand the meaning of the content cora.tx and cora.allx.
The shape is (2709x1433) (#41 nodes x #features), so apparently, there are 1433 node features.
The print is as follows(0, 19) 1.0
(0, 81) 1.0
(0, 146) 1.0
(0, 315) 1.0
(0, 774) 1.0
(0, 877) 1.0
(0, 1194) 1.0
(0, 1247) 1.0
(0, 1274) 1.0
(1, 19) 1.0
(1, 88) 1.0
(1, 149) 1.0
(1, 212) 1.0
(1, 233) 1.0How can we interpret the rows? I can't make sense of it.
This format is usually an edge list format, but as we have node features, how do the edges come into play?I looked into your other repositories and issues around this topic and couldn't find anything which helps me understand the structure of the .tx and .allx files.
tkipf/gcn#36
tkipf/gcn#125
tkipf/gcn#114
tkipf/gcn#36
tkipf/gcn#22
#35I'm planning to use node degree as recommended here tkipf/gcn#22 (and add more features later on)
My current attempt is to donode_deg = dict(G.degree()).values()
features = sparse.csr_matrix(node_deg).TAs I don't understand the Cora output, I can't really assess if that is correct or not.
Could you provide more guidance and explanation for that?
That would be great :)
Thank you in advance,
Best,
Minh
hi ,have you ever tried the degree matrix as the feature matrix, if so ,the dimention of feature matrix is n*1, does it work in your case?
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Related Issues (20)
- How could I get the output graph? HOT 1
- invoice or id card structure prediction
- Can GAE work in inductive setting? HOT 1
- Graph embedding vs Node embedding
- Does it work with Tensorflow 2 and Python 3? HOT 3
- Enforcing Sparsity
- Use continuous feature values HOT 1
- A question about negative samples generation in preprocessing.py HOT 4
- How can I reproduce the experiment using only the adjacency matrix without node features? HOT 2
- Potential Problem about the KL term? HOT 1
- About reduce links of the graph.
- KL divergence loss
- Descriptions of x, tx, allx?
- About Dataset Splits
- No such file or directory: 'data/ind.cora.x'
- Experiment with the weighted adjacency matrix
- Issues in preprocessing.py (not 100% sure)
- How to visualize the latent space?
- How to use GAE to generate nodes embeddings
- Problems I encountered in the train.py program HOT 1
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