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`HeteroData.subgraph()`
See original GitHub issue🚀 The feature, motivation and pitch
Similar to Data.subgraph(), there should exist a HeteroData.subgraph() method to compute subgraphs in a heterogeneous graph setting, e.g., for obtaining inductive node splits. Here, mask/index should be of type dict, holding masks/indices for each/a subset of node types:
hetero_data.subgraph({'paper': mask})
Alternatives
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Additional context
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Issue Analytics
- State:
- Created 2 years ago
- Reactions:3
- Comments:9 (6 by maintainers)
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torch_geometric.data — pytorch_geometric documentation
Returns the induced subgraph containing the node types and corresponding nodes in subset_dict . data = HeteroData() data['paper'].x = ... data['author'].x ...
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Overall, all heterogeneous graph loaders will produce a HeteroData object as output, holding a subset of the original data, ...
Read more >Graph.subgraph — NetworkX 2.8.8 documentation
Returns a SubGraph view of the subgraph induced on nodes . The induced subgraph of the graph contains the nodes in nodes and...
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from torch_geometric.data import Data, HeteroData class SeqData(HeteroData): def __init__(self): super().__init__() def __inc__(self, key, ...
Read more >torch-geometric Changelog
Added `HeteroData.subgraph` functionality ([4243](https://github.com/pyg-team/pytorch_geometric/pull/4243)) - Added the `MaskLabel` module and a ...
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Pinging @mananshah99 and @sdulloor here who shared interest in contributing this feature as well.
It depends on which data you want to train on. If you want to shrink the data prior to training, then
HeteroData.subgraphwould be applicable to create a smaller subgraph from your original graph. If you just want to operate on smaller batches during training, then you may want to adjust thebatch_sizeargument of a loader.Let me know if that makes sense to you.