Comments (3)
vbapst
commented on May 16, 2024
I guess the wider question here is what kind of learning algorithm do you plan to use?
You could for instance fully connect your initial graph, adding a marker for the edges in the source graph, and then try to predict the edges that are different in the target graph ?
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FilippNikitin
commented on May 16, 2024
@vbapst
Thank you so much for your answer. I've had the same idea as yours (using fully connected graphs), but I haven't checked the approach.
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alvarosg
commented on May 16, 2024
Closing assuming the last answer solved the problem due to inactivity. Please feel free to reopen if necessary.
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Related Issues (20)
- GraphTuple from batched tensors does not offset Senders/Receivers HOT 3
- Support Apple Silicon HOT 6
- Error while importing sonnet about gast HOT 1
- AttributeError: module 'sonnet' has no attribute 'AbstractModule' HOT 3
- issue with passing *_model_kwargs parameter HOT 2
- how to build an heterogeneous graph network? HOT 3
- Question about repeat implementation HOT 2
- Inference - shortest path demo HOT 1
- What's the difference between graph_nets and jraph? HOT 3
- Performance issue in /graph_nets/tests (by P3) HOT 2
- Error when calling trained model: "AttributeError: tuple object has no attribute "as_list" HOT 3
- Performance issue HOT 9
- Training on batches of GraphsTuples? HOT 5
- Is this project still live? HOT 2
- Error while using the placeholder function from utils_tf HOT 1
- ZeroDivsion error? HOT 2
- no output from processor HOT 6
- Issue with understanding HOT 2
- TensorFlow 1 is not supported in Google Colab HOT 1
- Problem with plot_compare_graphs function HOT 1
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