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--- |
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license: mit |
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task_categories: |
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- graph-ml |
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--- |
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# Dataset Card for MNIST |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [External Use](#external-use) |
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- [PyGeometric](#pygeometric) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Properties](#data-properties) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Additional Information](#additional-information) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **[Homepage](https://github.com/graphdeeplearning/benchmarking-gnns)** |
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- **Paper:**: (see citation) |
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### Dataset Summary |
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The `MNIST` dataset consists of 55000 images in 10 classes, represented as graphs. It comes from a computer vision dataset. |
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### Supported Tasks and Leaderboards |
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`MNIST` should be used for multiclass graph classification. |
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## External Use |
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### PyGeometric |
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To load in PyGeometric, do the following: |
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```python |
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from datasets import load_dataset |
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from torch_geometric.data import Data |
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from torch_geometric.loader import DataLoader |
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dataset_hf = load_dataset("graphs-datasets/<mydataset>") |
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# For the train set (replace by valid or test as needed) |
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dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]] |
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dataset_pg = DataLoader(dataset_pg_list) |
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``` |
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## Dataset Structure |
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### Data Properties |
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| property | value | |
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|---|---| |
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| #graphs | 55,000 | |
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| average #nodes | 70.6 | |
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| average #edges | 564.5 | |
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### Data Fields |
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Each row of a given file is a graph, with: |
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- `node_feat` (list: #nodes x #node-features): nodes |
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- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges |
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- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features |
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- `y` (list: #labels): contains the number of labels available to predict |
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- `num_nodes` (int): number of nodes of the graph |
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- `pos` (list: 2 x #node): positional information of each node |
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### Data Splits |
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This data is split. It comes from the PyGeometric version of the dataset. |
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## Additional Information |
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### Licensing Information |
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The dataset has been released under MIT license. |
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### Citation Information |
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``` |
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@article{DBLP:journals/corr/abs-2003-00982, |
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author = {Vijay Prakash Dwivedi and |
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Chaitanya K. Joshi and |
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Thomas Laurent and |
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Yoshua Bengio and |
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Xavier Bresson}, |
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title = {Benchmarking Graph Neural Networks}, |
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journal = {CoRR}, |
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volume = {abs/2003.00982}, |
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year = {2020}, |
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url = {https://arxiv.org/abs/2003.00982}, |
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eprinttype = {arXiv}, |
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eprint = {2003.00982}, |
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timestamp = {Sat, 23 Jan 2021 01:14:30 +0100}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2003-00982.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
