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about graph #1

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chostorjax opened this issue May 6, 2023 · 6 comments
Open

about graph #1

chostorjax opened this issue May 6, 2023 · 6 comments

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@chostorjax
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Hello! I want to know that when you predict in a timestep,did you use one P and one Q to predict one V and &?

@mukhlishga
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Hi, sorry it's been a while so I need to remember it correctly. But if I'm not mistaken, yeah that's correct.

@chostorjax
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Thanks for your answer! I already know by learning your code.Do you still working something about graph neural network?your code is perfect for me to learn graph neural network.

@mukhlishga
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Hi, nice to know that. I am now fully in software engineering, so rarely touch ML. You can see my other repos where I coded gnn from scratch instead of using built-in framework like the one I used here. There you can see the step-by-step gnn algorithm. Hope that helps

@Ayomi993
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Hi. Thank you for this! Can you please share the references and resources you used to learn more about GNN/GCN theory and programming in Python? I could not find something I could use to learn to develop something similar to your code.

Thanks in advance.

@mukhlishga
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I still remember it like yesterday that this GNN resource from Stanford was like the gold mine of GNN, truly helped me get through my thesis life.
https://www.youtube.com/watch?v=w9phpqZBt7k&list=PLQjfNCRPCjmZYPsZkjKVm6xoFqzFYWjTI&index=11&ab_channel=AbenDu
You can watch the overall lecture about graph ML in the playlist, but lecture no 8 and 9 is the core of GNN concept and its implementation using PyTorch

@Ayomi993
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I still remember it like yesterday that this GNN resource from Stanford was like the gold mine of GNN, truly helped me get through my thesis life. https://www.youtube.com/watch?v=w9phpqZBt7k&list=PLQjfNCRPCjmZYPsZkjKVm6xoFqzFYWjTI&index=11&ab_channel=AbenDu You can watch the overall lecture about graph ML in the playlist, but lecture no 8 and 9 is the core of GNN concept and its implementation using PyTorch

Thanks alot!

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3 participants