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README.md

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+# Dataset Card for IMDB-BINARY (IMDb-B)
+
+## 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://dl.acm.org/doi/10.1145/2783258.2783417)**
+- **[Repository](https://www.chrsmrrs.com/graphkerneldatasets/IMDB-BINARY.zip):**: 
+- **Paper:**: Deep Graph Kernels (see citation) 
+- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-classification-on-imdb-b)
+
+### Dataset Summary
+
+The `IMDb-B` dataset is "a movie collaboration dataset that consists of the ego-networks of 1,000 actors/actresses who played roles in movies in IMDB. In each graph, nodes represent actors/actress, and there is an edge between them if they appear in the same movie. These graphs are derived from the Action and Romance genres". 
+
+### Supported Tasks and Leaderboards
+
+`IMDb-B` should be used for graph classification (aiming to predict whether a movie graph is an action or romance movie), a binary classification task. The score used is accuracy, using a 10-fold cross-validation.
+
+
+## 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 |
+|---|---|
+| scale | medium |
+| #graphs | 1000 |
+| average #nodes | 19.79 |
+| average #edges | 193.25 |
+
+### Data Fields
+
+Each row of a given file is a graph, with: 
+- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
+- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
+- `num_nodes` (int): number of nodes of the graph
+
+### Data Splits
+
+This data comes from the PyGeometric version of the dataset.
+This information can be found back using
+```python
+from torch_geometric.datasets import TUDataset
+cur_dataset = TUDataset(root="../dataset/loaded/", 
+                               name="IMDB-BINARY")
+```
+
+## Additional Information
+
+### Licensing Information
+The dataset has been released under unknown license, please open an issue if you have this information.
+
+### Citation Information
+```
+@inproceedings{10.1145/2783258.2783417,
+author = {Yanardag, Pinar and Vishwanathan, S.V.N.},
+title = {Deep Graph Kernels},
+year = {2015},
+isbn = {9781450336642},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/2783258.2783417},
+doi = {10.1145/2783258.2783417},
+abstract = {In this paper, we present Deep Graph Kernels, a unified framework to learn latent representations of sub-structures for graphs, inspired by latest advancements in language modeling and deep learning. Our framework leverages the dependency information between sub-structures by learning their latent representations. We demonstrate instances of our framework on three popular graph kernels, namely Graphlet kernels, Weisfeiler-Lehman subtree kernels, and Shortest-Path graph kernels. Our experiments on several benchmark datasets show that Deep Graph Kernels achieve significant improvements in classification accuracy over state-of-the-art graph kernels.},
+booktitle = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
+pages = {1365–1374},
+numpages = {10},
+keywords = {collaboration networks, bioinformatics, r-convolution kernels, graph kernels, structured data, deep learning, social networks, string kernels},
+location = {Sydney, NSW, Australia},
+series = {KDD '15}
+}
+```
+
+### Contributions
+
+Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset.