graphs-datasets/MNIST

Dataset Card for MNIST

Table of Contents

• —Table of Contents
• —Dataset Description
• —Dataset Summary
• —Supported Tasks and Leaderboards
• —External Use
• —PyGeometric
• —Dataset Structure
• —Data Properties
• —Data Fields
• —Data Splits
• —Additional Information
• —Licensing Information
• —Citation Information
• —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:

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

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}
}