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Python library assists deep learning on graphs

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Hi,I was tring to transfer my deepsnap HeteroGraph object into a pytorch_geometric HeteroData Object, I wonder if there is any easy path to do it?

Hello, After upgrading from PyG 2.3.x to PyG 2.4.0, the `keys` property of `torch_geometric.data.data.BaseData` was refactored into a method, leading to the following error when calling `GraphDataset.pyg_to_graphs` ``` File [.../lib/python3.10/site-packages/deepsnap/dataset.py:1277](https://file+.vscode-resource.vscode-cdn.net/.../lib/python3.10/site-packages/deepsnap/dataset.py:1277),...

Hello everyone: I'm trying to learn the examples. All the example can run smoothly **except subgraph_matching.** **When I run exmaples\subgraph_matching\train.py** but i am getting an error: **TypeError: Transform function returns...

I am converting a custom PyG dataset in the following manner ```python pyg_dataset = GraphCountDataset( dataset_path, name="subgraph_counting", task_label_idx=self.task_label_index[task_key] ) mode_idxs = pyg_dataset.separate_data(seed=None, fold_idx=None) deepsnap_graphs = GraphDataset.pyg_to_graphs(pyg_dataset) dataset = GraphDataset(deepsnap_graphs, task="graph")...

It is very interesting that the work of deepsnap is to repeatedly convert other datasets into deepsnap datasets, but it does not provide the reading and saving interface of deepsnap...