metadata

licence: unknown
task_categories:
  - graph-ml

Dataset Card for AIDS

Table of Contents
Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
External Use
- PyGeometric
Dataset Structure
Additional Information

Dataset Description

Homepage
Paper:: (see citation)
Leaderboard:: Papers with code leaderboard

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:

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"
}