metadata
licence: unknown
license: gpl-3.0
task_categories:
- graph-ml
Dataset Card for Deezer ego nets
Table of Contents
Dataset Description
- Homepage
- Paper:: (see citation)
Dataset Summary
The Deezer ego nets dataset contains ego-nets of Eastern European users collected from the music streaming service Deezer in February 2020. Nodes are users and edges are mutual follower relationships.
Supported Tasks and Leaderboards
The related task is the binary classification to predict gender for the ego node in the graph.
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 Fields
Each row of a given file is a graph, with:
edge_index(list: 2 x #edges): pairs of nodes constituting edgesy(list: #labels): contains the number of labels available to predictnum_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 GPL-3.0 license.
Citation Information
See also github.
@inproceedings{karateclub,
title = {{Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs}},
author = {Benedek Rozemberczki and Oliver Kiss and Rik Sarkar},
year = {2020},
pages = {3125–3132},
booktitle = {Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM '20)},
organization = {ACM},
}