History of tgbl_comment
The changes to the source code of this entry in the repository, newest first, each with the differences it made to the entry's files. They are also listed on GitLab.
[entries] Add the date when each entry was added, and remove icon_hash beff7c9
The dates are those of the commits that first added the entries' files (2020-08-11 for the entries of the initial commit); renamed entries keep the date of their first name. Changed: none
diff --git a/entries/tgbl_comment/__init__.py b/entries/tgbl_comment/__init__.py
index 28726f9..47cf8aa 100644
--- a/entries/tgbl_comment/__init__.py
+++ b/entries/tgbl_comment/__init__.py
@@ -38,7 +38,7 @@ url = ['https://tgb.complexdatalab.com/docs/linkprop/',
'https://github.com/shenyangHuang/TGB']
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)', 'https://doi.org/10.48550/arXiv.2307.01026'),
('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)', 'https://doi.org/10.1145/3487553.3524699')]
-icon_hash = None
+date_added = '2026-10-27'
upstream_license = ('CC BY-NC', 'https://creativecommons.org/licenses/by-nc/4.0/')
upstream_prefix = 'https://object-arbutus.alliancecan.ca/swift/v1/14c95234f6cd4a21a47deafe20cce2a7/tgb'
files = [('tgbl-comment.zip:tgbl-comment_edgelist.csv', None, None)]
[entries] Prefix the properties in the descriptions with (Node), (Edge) or (Graph) bc3dbbd
The lists of properties at the end of the descriptions marked edge properties with "Edge" but left node properties unmarked, or grouped them in "Nodes: `a`, ...; `b`, ..." items, so the kind of a property was often unclear. Every item now starts with (Node), (Edge) or (Graph), followed by the property names, a colon and the explanation, one item per property or group of properties; qualifications (kinds of nodes, networks) follow the names in parentheses. Properties that were only named got a short explanation, and a few items that named the wrong kind of property were corrected.
diff --git a/entries/tgbl_comment/__init__.py b/entries/tgbl_comment/__init__.py
index a66a142..28726f9 100644
--- a/entries/tgbl_comment/__init__.py
+++ b/entries/tgbl_comment/__init__.py
@@ -28,11 +28,11 @@ description = """A temporal network of replies between the users of Reddit from
The network has the following properties:
-- `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)."""
+- (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']
url = ['https://tgb.complexdatalab.com/docs/linkprop/',
'https://github.com/shenyangHuang/TGB']
[entries] Remove unneeded code from the entries 2c69f19
Unneeded "global files" statements, unused imports and variables, f-strings without placeholders, duplicate keys in foodweb_little_rock (keeping the order and the values used), and the second definition of get_lines() in twitter_higgs. Nothing changes in the networks or descriptions. Changed: none
diff --git a/entries/tgbl_comment/__init__.py b/entries/tgbl_comment/__init__.py
index 640b302..a66a142 100644
--- a/entries/tgbl_comment/__init__.py
+++ b/entries/tgbl_comment/__init__.py
@@ -51,7 +51,6 @@ def fetch_upstream(force=False):
@coerce_props()
@annotate()
def parse(alts=None):
- global files
name = __name__.split(".")[-1]
for fname, alt, fmt in files:
if alts is not None and alt not in alts:
[entries] New entry: tgbl_comment 6c50f79
diff --git a/entries/tgbl_comment/__init__.py b/entries/tgbl_comment/__init__.py
new file mode 100644
index 0000000..640b302
--- /dev/null
+++ b/entries/tgbl_comment/__init__.py
@@ -0,0 +1,74 @@
+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2026 Tiago de Paula Peixoto <tiago@skewed.de>
+#
+# This program is free software: you can redistribute it and/or modify it under
+# the terms of the GNU Affero General Public License as published by the Free
+# Software Foundation, either version 3 of the License, or (at your option) any
+# later version.
+#
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more
+# details.
+#
+# You should have received a copy of the GNU Affero General Public License along
+# with this program. If not, see <http://www.gnu.org/licenses/>.
+
+
+
+import pandas
+
+from .. import *
+
+
+title = "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:
+
+- `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']
+url = ['https://tgb.complexdatalab.com/docs/linkprop/',
+ 'https://github.com/shenyangHuang/TGB']
+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)', 'https://doi.org/10.48550/arXiv.2307.01026'),
+ ('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)', 'https://doi.org/10.1145/3487553.3524699')]
+icon_hash = None
+upstream_license = ('CC BY-NC', 'https://creativecommons.org/licenses/by-nc/4.0/')
+upstream_prefix = 'https://object-arbutus.alliancecan.ca/swift/v1/14c95234f6cd4a21a47deafe20cce2a7/tgb'
+files = [('tgbl-comment.zip:tgbl-comment_edgelist.csv', None, None)]
+
+def fetch_upstream(force=False):
+ return fetch_upstream_files(__name__.split(".")[-1], upstream_prefix, files,
+ force)
+
+@cache_network()
+@coerce_props()
+@annotate()
+def parse(alts=None):
+ global files
+ name = __name__.split(".")[-1]
+ for fname, alt, fmt in files:
+ if alts is not None and alt not in alts:
+ continue
+ # "ts,src,dst,subreddit,num_words,score"
+ with open_upstream_file(name, fname, "rb") as f:
+ df = pandas.read_csv(f, dtype="int64")
+ ids, idx = numpy.unique(numpy.concatenate([df["src"].values,
+ df["dst"].values]),
+ return_inverse=True)
+ E = len(df)
+ g = Graph(directed=True)
+ g.add_vertex(len(ids))
+ g.vp.user_id = g.new_vp("int64_t", vals=ids)
+ g.add_edge_list(numpy.column_stack([idx[:E], idx[E:]]))
+ del idx
+ g.ep.time = g.new_ep("int64_t", vals=df["ts"].values)
+ for k in ["subreddit", "num_words", "score"]:
+ g.ep[k] = g.new_ep("int", vals=df[k].values)
+ yield alt, g