| | --- |
| | license: gpl-3.0 |
| | task_categories: |
| | - graph-ml |
| | --- |
| | |
| | # Dataset Card for Reddit threads |
| |
|
| | ## 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://snap.stanford.edu/data/reddit_threads.html)** |
| | - **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: |
| |
|
| | ```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 |
| | ### 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](https://github.com/benedekrozemberczki/karateclub). |
| |
|
| | ``` |
| | @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}, |
| | } |
| | ``` |
