tgbl_comment — Reddit comment replies (tgbl-comment, TGB, 2005-2010)

Description

A temporal network of replies between the users of Reddit from 2005 to 2010, from the tgbl-comment dataset of the Temporal Graph Benchmark (TGB), extracted from the Reddit data of Nadiri and Takes (2022), based on the Pushshift collection of Reddit comments. Each node is a user, and each timestamped directed edge is a comment of a user in reply to a post or comment of another user (or of the same user: there are self-loops). The users are identified only by indices (the anonymized author identifiers of the source data), and the subreddits only by indices in the order of their first appearance (their names are not given).

The network has the following properties:

(Node) user_id: the identifier of the user in the source data.
(Edge) time: the time of the comment, in Unix time.
(Edge) subreddit: the index of the subreddit of the comment.
(Edge) num_words: the number of words of the comment.
(Edge) score: the score of the comment (upvotes minus downvotes).

Tags

Social Online Communication 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/linkprop/
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
tgbl_comment	994,790	44,314,507	44.55	569.83	1665.73	1662.26	0.08	0.12	31	0.76	Directed	Unipartite	user_id	time subreddit num_words score	337.5 MiB	514.7 MiB	495.0 MiB	487.0 MiB