task_categories:graph-ml arxiv:2007.08663 region:us
---
licence: unknown
task_categories:
- graph-ml
---

# Dataset Card for AIDS

## 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://wiki.nci.nih.gov/display/NCIDTPdata/AIDS+Antiviral+Screen+Data)**
- **Paper:**:  (see citation)
- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-classification-on-aids)

### Dataset Summary
The `AIDS` dataset is a dataset containing compounds checked for evidence of anti-HIV activity..

 ### Supported Tasks and Leaderboards
`AIDS` should be used for molecular classification, a binary classification task. The score used is accuracy with 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 | 1999 |
| average #nodes | 15.5875 |
| average #edges | 32.39 |

### 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: 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 is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.

## Additional Information

### Licensing Information
The dataset has been released under license unknown.

### Citation Information
```
@inproceedings{Morris+2020,
    title={TUDataset: A collection of benchmark datasets for learning with graphs},
    author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
    booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
    archivePrefix={arXiv},
    eprint={2007.08663},
    url={www.graphlearning.io},
    year={2020}
}
```

```

@InProceedings{10.1007/978-3-540-89689-0_33,
    author="Riesen, Kaspar
    and Bunke, Horst",
    editor="da Vitoria Lobo, Niels
    and Kasparis, Takis
    and Roli, Fabio
    and Kwok, James T.
    and Georgiopoulos, Michael
    and Anagnostopoulos, Georgios C.
    and Loog, Marco",
    title="IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning",
    booktitle="Structural, Syntactic, and Statistical Pattern Recognition",
    year="2008",
    publisher="Springer Berlin Heidelberg",
    address="Berlin, Heidelberg",
    pages="287--297",
    abstract="In recent years the use of graph based representation has gained popularity in pattern recognition and machine learning. As a matter of fact, object representation by means of graphs has a number of advantages over feature vectors. Therefore, various algorithms for graph based machine learning have been proposed in the literature. However, in contrast with the emerging interest in graph based representation, a lack of standardized graph data sets for benchmarking can be observed. Common practice is that researchers use their own data sets, and this behavior cumbers the objective evaluation of the proposed methods. In order to make the different approaches in graph based machine learning better comparable, the present paper aims at introducing a repository of graph data sets and corresponding benchmarks, covering a wide spectrum of different applications.",
    isbn="978-3-540-89689-0"
}


```