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View Code? Open in Web Editor NEWThis repository implements variational graph auto encoder by Thomas Kipf.
License: MIT License
This repository implements variational graph auto encoder by Thomas Kipf.
License: MIT License
Hello, Thanks for releasing your work! I have seen that you said you use GAE for node classification tasks but can not get good results, have you found why? And could you plz release the code for node classification?
Hi, I have been trying to recreate the adjacency of my sparse matrix using the same VGAE concept. I am not able to recreate the adjacency matrix. Do you think there is any preprocessing is necessary for such sparse graphs? Please let me know. I am attaching the data and code for your reference. I am also attaching the results I am able to reproduce using this code. Please feel free to go through the code and suggest necessary changes. Thank you!
P.S: The graphs are unidirectional. And do not have self-loops as well.
[states.zip](https://github.com/Daehan
adjacency_pred_vgae .txt
Kim/vgae_pytorch/files/8883726/states.zip)
How do I turn other data into data that this code can use?
Hello, now if I have many graphs, how can train these data? It seems that the code can just train on one graph data.
kl_divergence = 0.5/ A_pred.size(0) * (1 + 2*model.logstd - model.mean**2 - torch.exp(model.logstd)).sum(1).mean()
torch.exp(model.logstd)**2 ?
Hello, thank you for your great work.
I'm new to graph networks, and I would appreciate it if you'd explain to me if this model can be used for clustering i.e. community detection? if not, can you recommend ways to employ a GCN or a GAE for community detection?
Hi,
I am having some trouble understanding why are we using the normalization constants norm and weight_tensor and why they are defined in this way. Could you provide some intuition behind this normalization?
norm = adj.shape[0] * adj.shape[0] / float((adj.shape[0] * adj.shape[0] - adj.sum()) * 2)
pos_weight = float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum()
weight_mask = adj_label.to_dense().view(-1) == 1
weight_tensor = torch.ones(weight_mask.size(0))
weight_tensor[weight_mask] = pos_weight
Thanks a lot for your help beforehand!
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