diff --git a/entries/tgbn_reddit/__init__.py b/entries/tgbn_reddit/__init__.py
index 0c95f51..dbefe5f 100644
--- a/entries/tgbn_reddit/__init__.py
+++ b/entries/tgbn_reddit/__init__.py
@@ -39,7 +39,7 @@ url = ['https://tgb.complexdatalab.com/docs/nodeprop/',
        '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 = [('tgbn-reddit.zip:tgbn-reddit_edgelist.csv', None, None)]

diff --git a/entries/tgbn_reddit/__init__.py b/entries/tgbn_reddit/__init__.py
index 8f0f296..0c95f51 100644
--- a/entries/tgbn_reddit/__init__.py
+++ b/entries/tgbn_reddit/__init__.py
@@ -28,12 +28,12 @@ description = """A temporal bipartite network of the users of Reddit and the sub
 
 The network has the following properties:
 
-- `is_subreddit`: whether the node is a subreddit (otherwise a user).
-- `name`: the name of the subreddit (empty for users).
-- `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)."""
+- (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']
 url = ['https://tgb.complexdatalab.com/docs/nodeprop/',
        'https://github.com/shenyangHuang/TGB']

diff --git a/entries/tgbn_reddit/__init__.py b/entries/tgbn_reddit/__init__.py
index 9e12923..8f0f296 100644
--- a/entries/tgbn_reddit/__init__.py
+++ b/entries/tgbn_reddit/__init__.py
@@ -52,7 +52,6 @@ def fetch_upstream(force=False):
 @coerce_props(vforce={"name": "string"})
 @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:

diff --git a/entries/tgbn_reddit/__init__.py b/entries/tgbn_reddit/__init__.py
new file mode 100644
index 0000000..9e12923
--- /dev/null
+++ b/entries/tgbn_reddit/__init__.py
@@ -0,0 +1,81 @@
+#! /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 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:
+
+- `is_subreddit`: whether the node is a subreddit (otherwise a user).
+- `name`: the name of the subreddit (empty for users).
+- `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']
+url = ['https://tgb.complexdatalab.com/docs/nodeprop/',
+       '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 = [('tgbn-reddit.zip:tgbn-reddit_edgelist.csv', None, None)]
+
+def fetch_upstream(force=False):
+    return fetch_upstream_files(__name__.split(".")[-1], upstream_prefix, files,
+                                force)
+
+@cache_network()
+@coerce_props(vforce={"name": "string"})
+@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,user,subreddit,num_words,score"
+        with open_upstream_file(name, fname, "rb") as f:
+            df = pandas.read_csv(f, dtype={"ts": "int64", "user": "int64",
+                                           "subreddit": "category",
+                                           "num_words": "int64", "score": "int64"},
+                                 keep_default_na=False)
+        users, uidx = numpy.unique(df["user"].values, return_inverse=True)
+        subs = df["subreddit"].cat.categories
+        N = len(users)
+        g = Graph(directed=True)
+        g.add_vertex(N + len(subs))
+        g.vp.is_subreddit = g.new_vp("bool")
+        g.vp.is_subreddit.a[N:] = True
+        g.vp.name = g.new_vp("string", vals=[""] * N + list(subs))
+        g.vp.user_id = g.new_vp("int64_t", val=-1)
+        g.vp.user_id.a[:N] = users
+        g.add_edge_list(numpy.column_stack([uidx, N + df["subreddit"].cat.codes.values]))
+        del uidx
+        g.ep.time = g.new_ep("int64_t", vals=df["ts"].values)
+        for k in ["num_words", "score"]:
+            g.ep[k] = g.new_ep("int", vals=df[k].values)
+        yield alt, g