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