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Make edges in HeterohilousGraphDataset undirected #7065

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merged 5 commits into from
Mar 29, 2023
Merged

Make edges in HeterohilousGraphDataset undirected #7065

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2592128
convert heterophilous graphs to undirected
OlegPlatonov Mar 28, 2023
7f5d45e
Update torch_geometric/datasets/heterophilous_graph_dataset.py
rusty1s Mar 29, 2023
21917db
Update torch_geometric/datasets/heterophilous_graph_dataset.py
rusty1s Mar 29, 2023
3dfcc48
update
rusty1s Mar 29, 2023
93a71f0
Merge branch 'master' into heterophilous_graphs_undirected
rusty1s Mar 29, 2023
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).

### Changed

- Edges in `HeterophilousGraphDataset` are now undirected by default ([#7065](https://github.com/pyg-team/pytorch_geometric/pull/7065))
- Fixed a bug in `FastHGTConv` that computed values via parameters used to compute the keys ([#7050](https://github.com/pyg-team/pytorch_geometric/pull/7050))
- Accelerated sparse tensor conversion routines ([#7042](https://github.com/pyg-team/pytorch_geometric/pull/7042), [#7043](https://github.com/pyg-team/pytorch_geometric/pull/7043))
- Change `torch_sparse.SparseTensor` logic to utilize `torch.sparse_csr` instead ([#7041](https://github.com/pyg-team/pytorch_geometric/pull/7041))
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2 changes: 2 additions & 0 deletions torch_geometric/datasets/heterophilous_graph_dataset.py
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Expand Up @@ -5,6 +5,7 @@
import torch

from torch_geometric.data import Data, InMemoryDataset, download_url
from torch_geometric.utils import to_undirected


class HeterophilousGraphDataset(InMemoryDataset):
Expand Down Expand Up @@ -111,6 +112,7 @@ def process(self):
x = torch.from_numpy(raw['node_features'])
y = torch.from_numpy(raw['node_labels'])
edge_index = torch.from_numpy(raw['edges']).t().contiguous()
edge_index = to_undirected(edge_index, num_nodes=x.size(0))
train_mask = torch.from_numpy(raw['train_masks']).t().contiguous()
val_mask = torch.from_numpy(raw['val_masks']).t().contiguous()
test_mask = torch.from_numpy(raw['test_masks']).t().contiguous()
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