Comments (3)
Thanks for your message. The graph nets library does not currently support heterogeneous graphs in a single GraphsTuple
.
The simple option would be to keep two GraphsTuple
s one with each of the types of edges, and with the same nodes. And then use the blocks.broadcasters/aggregator
ops to build a model similar to the existing blocks.NodeUpdate
and blocks.EdgeUpdate` that operates on both graphs in parallel.
Some options:
- I suspect the model may also work if you simply merge all edges into a single type, if all you are looking for informal reproducibility.
- At the edge update, you may want to use two separate MLPs for each edge type, by storing the edge type, then mask out the outputs corresponding to the wrong type for each MLP, and concatenate them side by side.
- Implementing the exact same baseline in an efficient way would require using the low level API of the GraphNets library as indicated above.
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@alvarosg thanks for your help.I will try to implement your advise approach.
Besides, may i ask you a question? When will you publish the code of the paper?
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Code is now available here https://github.com/deepmind/deepmind-research/tree/master/meshgraphnets :)
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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
- 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
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