graphs-datasets/CSL

Dataset Card for CSK

Dataset Summary

The CSL dataset is a synthetic dataset, to test GNN expressivity.

Supported Tasks and Leaderboards

CSL should be used for binary graph classification, on isomoprhism or not.

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	150
average #nodes	41.0
average #edges	164.0

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

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}
}