New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Recreating the adjacancey matrix using VGAE cocept #7
Comments
What is the purpose of reconstructing your adjacency matrix? Since you have a sparse graph, there are not much training signals for some nodes, resulting in inaccurate edge reconstruction. Maybe you need some auxiliary approaches to model your dataset. |
I am trying to create a custom policy for my reinforcement learning agent to train with. i am generating this data from my reinforcement learning environment. what kind of auxilary approaches should i be using? can you throw some light on them? |
Why don't you use true adjacency matrix as a reward signal, instead of reconstructing it? I don't have much to tell about auxiliary approaches since I have no clue on your task. Can you elaborate more on that? |
So, what exactly I am doing is training the The |
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)
The text was updated successfully, but these errors were encountered: