README.md

metadata

license: gpl-3.0
task_categories:
  - graph-ml

Dataset Card for Reddit threads

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
• External Use
  • PyGeometric
• Dataset Structure
  • Data Properties
  • Data Fields
  • Data Splits
• Additional Information
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage
• Paper:: (see citation)

Dataset Summary

The Reddit threads dataset contains 'discussion and non-discussion based threads from Reddit which we collected in May 2018. Nodes are Reddit users who participate in a discussion and links are replies between them' (doc).

Supported Tasks and Leaderboards

The related task is the binary classification to predict whether a thread is discussion based or not.

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

Dataset information

• 203,088 graphs

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: #labels): contains the number of labels available to predict
• 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 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},
}