Bridging the gap between DGL and PyG #6979
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I like the idea of providing conversion from DGL to PyG and vice versa. This should be pretty straightforward to get in. Do you have interest in contributing this? |
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Hello, I am new to contributing to PyG, but this issue feels like a good one to start. Can I work on it? |
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I am not sure if @nowyouseemejoe has the intention to work on this. I think we could split the work into two parts:
such that both of you could work on this in parallel. WDYT? |
rusty1s
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Mar 29, 2023
Issue #6979 ## Description I add two functions `from_dgl(dgl.Graph) -> data: Union[Data, HeteroData]`and `to_dgl(Union[Data, HeteroData]) -> dgl.Graph` in `utils/convert.py`. Both function in both direction performs the same three operations, setting the node and edge attributes and copies the edges of the underlying graph(s). --------- Co-authored-by: Jintang Li <cnljt@outlook.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: rusty1s <matthias.fey@tu-dortmund.de>
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did anyone solved it ? |
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🚀 The feature, motivation and pitch
DGL and PyG are two popular deep learning libraries that specialize in working with graph-structured data. Both DGL and PyG have become go-to libraries for researchers and practitioners working on graph deep learning problems, and have a growing community of contributors and users.
I would like to request a new feature for PyG that allows for converting DGL graphs to PyG instances (e.g.,
Data). This would make it easier for users who have existing graph structures in DGL to switch to PyG without having to recreate their graphs from scratch.In addition, it would be great to have a tutorial added to the documentation, demonstrating how to mitigate from DGL to PyG using this new feature. The tutorial could introduce some equivalent message and aggregation operations for these two libraries, helping users navigate the differences between them. With this new feature and tutorial, users can seamlessly transition between DGL and PyG, unlocking the full potential of graph deep learning for their projects.
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Additional context
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