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README.md

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----
-license: unknown
----
-
-# 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.

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