Datasets:
Search is not available for this dataset
node_feat
sequence | edge_index
sequence | edge_attr
sequence | y
sequence | pos
sequence | num_nodes
int64 40
75
|
|---|---|---|---|---|---|
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End of preview. Expand
in Dataset Viewer.
Dataset Card for MNIST
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
| 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): nodesedge_index(list: 2 x #edges): pairs of nodes constituting edgesedge_attr(list: #edges x #edge-features): for the aforementioned edges, contains their featuresy(list: #labels): contains the number of labels available to predictnum_nodes(int): number of nodes of the graphpos(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}
}
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