| | --- |
| | license: mit |
| | task_categories: |
| | - graph-ml |
| | --- |
| | |
| | # Dataset Card for CSK |
| |
|
| | ## 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 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: |
| |
|
| | ```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 | 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} |
| | } |
| | |
| | ``` |
