Dataset Card for IMDB-BINARY (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:

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

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 for adding this dataset.