tgbn_reddit — Reddit users and the subreddits they post in (tgbn-reddit, TGB, 2008-2010)

Description

A temporal bipartite network of the users of Reddit and the subreddits they posted in, from January 2008 to December 2010, from the tgbn-reddit dataset of the Temporal Graph Benchmark (TGB), extracted from the Reddit data of Nadiri and Takes (2022), based on the Pushshift collection of Reddit posts and comments (the TGB paper states that the data span 2005 to 2019, but the times of the data span 2008 to 2010). Each timestamped edge from a user to a subreddit is a post (or comment) of the user in the subreddit, with its number of words and its score. The users are identified only by indices (the anonymized author identifiers of the source data).

The network has the following properties:

(Node) is_subreddit: whether the node is a subreddit (otherwise a user).
(Node) name: the name of the subreddit (empty for users).
(Node) user_id: the identifier of the user in the source data (-1 for subreddits).
(Edge) time: the time of the post, in Unix time.
(Edge) num_words: the number of words of the post.
(Edge) score: the score of the post (upvotes minus downvotes).

Tags

Social Online Affiliation Unweighted Multigraph Timestamps Metadata

Added

2026-10-27

Citation

S. Huang, F. Poursafaei, J. Danovitch, M. Fey, W. Hu, E. Rossi, J. Leskovec, M. Bronstein, G. Rabusseau, R. Rabbany, "Temporal Graph Benchmark for Machine Learning on Temporal Graphs." Advances in Neural Information Processing Systems 36 (Datasets and Benchmarks Track) (2023) 10.48550/arXiv.2307.01026 Sci-Hub
A. Nadiri, F. W. Takes, "A large-scale temporal analysis of user lifespan durability on the Reddit social media platform." Companion Proceedings of the Web Conference 2022, 677-685 (2022) 10.1145/3487553.3524699 Sci-Hub

Upstream URL OK

https://tgb.complexdatalab.com/docs/nodeprop/
https://github.com/shenyangHuang/TGB

Upstream license

CC BY-NC, https://creativecommons.org/licenses/by-nc/4.0/

Networks

Tip: hover over a column header for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
tgbn_reddit	11,766	27,174,118	2309.55	57803.85	460.88	10.01	-0.34	0.00	4	1.00	Directed	Bipartite	is_subreddit name user_id	time num_words score	138.1 MiB	219.5 MiB	211.1 MiB	186.9 MiB