|
--- |
|
license: unknown |
|
task_categories: |
|
- graph-ml |
|
--- |
|
|
|
# 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. |
