Open Graph Benchmark Lite (ogb_lite) is a subset of the ogb project. It supports library-agnostic loaders and it does not require torch.
Project description
Open Graph Benchmark Lite (ogb_lite)
Open Graph Benchmark Lite (ogb_lite) is a subset of the ogb project. It supports library-agnostic loaders and it does not require torch.
99.99% of the code is copied from the OGB project:
https://github.com/snap-stanford/ogb <https://github.com/snap-stanford/ogb>
_
We only make some small changes such that you can use ogb_lite without installing torch.
Installation
.. code-block:: bash
pip install ogb_lite
Tutorial
ogb_lite only contains three library-agnostic loaders: NodePropPredDataset
\ , LinkPropPredDataset
\ , and GraphPropPredDataset
.
NodePropPredDataset:
.. code-block:: python
coding=utf-8
from ogb_lite.nodeproppred import NodePropPredDataset
dataset = NodePropPredDataset(name="ogbn-proteins")
split_idx = dataset.get_idx_split() train_idx, valid_idx, test_idx = split_idx["train"], split_idx["valid"], split_idx["test"] graph, label = dataset[0] # graph: library-agnostic graph object
print(graph, label) print(train_idx, valid_idx, test_idx)
LinkPropPredDataset:
.. code-block:: python
coding=utf-8
from ogb_lite.linkproppred import LinkPropPredDataset
dataset = LinkPropPredDataset(name="ogbl-ppa")
split_edge = dataset.get_edge_split() train_edge, valid_edge, test_edge = split_edge["train"], split_edge["valid"], split_edge["test"] graph = dataset[0] # graph: library-agnostic graph object
print(graph) print(train_edge, valid_edge, test_edge)
GraphPropPredDataset:
.. code-block:: python
coding=utf-8
from ogb_lite.graphproppred import GraphPropPredDataset
dataset = GraphPropPredDataset(name="ogbg-molhiv")
split_idx = dataset.get_idx_split() train_idx, valid_idx, test_idx = split_idx["train"], split_idx["valid"], split_idx["test"]
graph, label = dataset[0] # graph: library-agnostic graph object print(graph, label) print(train_idx, valid_idx, test_idx)
Citing OGB
If you use OGB datasets in your work, please cite the OGB paper (Bibtex below).
.. code-block::
@article{hu2020ogb, title={Open Graph Benchmark: Datasets for Machine Learning on Graphs}, author={Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec}, journal={arXiv preprint arXiv:2005.00687}, year={2020} }
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