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+#! /usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (C) 2020-2026 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/>.
+
+
+from .. import *
+
+import calendar
+import time
+
+title = "FilmTrust and CiaoDVD trust and rating networks (2011-2013)"
+description = r"""Trust networks and ratings of users of two movie websites, crawled by G. Guo and collaborators for research on trust-aware recommender systems, and distributed with the LibRec library:
+
+- FilmTrust, a movie sharing and rating website, crawled in its entirety in June 2011. Users rate movies, and can declare that they trust other users.
+- CiaoDVD, the DVD category of the British consumer review website Ciao (dvd.ciao.co.uk), crawled in November and December 2013. Users write reviews of movies with a rating, rate the helpfulness of other users' reviews, and declare which other users they trust. Movies without any review were left out by the authors.
+
+User and movie identifiers were anonymized; the identifiers of users are the same in the networks of one website. There are five networks:
+
+- `filmtrust_trust`: a directed network of users, where an edge $i \to j$ means that user $i$ trusts user $j$.
+- `filmtrust_ratings`: a bipartite network of users and the movies they rated, with the rating. Three user-movie pairs appear twice in the data (with the same or a different rating); all rows are kept as edges.
+- `ciaodvd_trust`: a directed network of users, where an edge $i \to j$ means that user $i$ trusts user $j$.
+- `ciaodvd_ratings`: a bipartite network of users and the movies they reviewed, with the rating and the date of the review. A user may have reviewed a movie more than once (320 user-movie pairs appear more than once); each review is an edge.
+- `ciaodvd_review_ratings`: a directed multigraph of users, where an edge $i \to j$ means that user $i$ rated the helpfulness of a review written by user $j$ (the author of the review is given by `ciaodvd_ratings`). Each rating is an edge. Users who did not write reviews or rate them appear only in the trust network.
+
+The trust values given in the data are all 1, and are therefore left out.
+
+The networks have the following properties:
+
+- `user_id` (users), `movie_id` (movies): the anonymized identifiers in the data; `is_movie` (bipartite networks), whether the node is a movie (otherwise a user).
+- `ciaodvd_ratings`, vertex `genre`: the numeric identifier of the movie's genre (one of 17; their names are not given, -1 for users).
+- `filmtrust_ratings`, edge `rating`: the rating of the movie (from 0.5 to 4, in steps of 0.5).
+- `ciaodvd_ratings`, edges: `rating`, the rating of the movie (1 to 5); `review_id`, the identifier of the review; `time`, the date of the review, as the Unix time of 00:00 UTC of that day.
+- `ciaodvd_review_ratings`, edges: `review_id`, the identifier of the rated review; `helpfulness`, the rating of the review: 0 (off topic), 1 (not helpful), 2 (somewhat helpful), 3 (helpful), 4 (very helpful) or 5 (exceptional)."""
+tags = ['Social', 'Online', 'Preferences', 'Weighted', 'Multigraph', 'Timestamps', 'Metadata']
+url = ['https://guoguibing.github.io/librec/datasets.html']
+citation = [('G. Guo, J. Zhang, and N. Yorke-Smith, "A Novel Bayesian Similarity Measure for Recommender Systems." Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2619-2625 (2013).', 'https://www.ijcai.org/Proceedings/13/Papers/386.pdf'),
+            ('G. Guo, J. Zhang, D. Thalmann, and N. Yorke-Smith, "ETAF: An Extended Trust Antecedents Framework for Trust Prediction." Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 540-547 (2014).', 'https://doi.org/10.1109/ASONAM.2014.6921639')]
+icon_hash = None
+upstream_license = None
+upstream_prefix = 'https://raw.githubusercontent.com/guoguibing/guoguibing.github.io/ef318698918b3bcb5b299074ce6e18eef564ceff/librec/datasets'
+files = [('filmtrust.zip:trust.txt', 'filmtrust_trust', None),
+         ('filmtrust.zip:ratings.txt', 'filmtrust_ratings', None),
+         ('CiaoDVD.zip:trusts.txt', 'ciaodvd_trust', None),
+         ('CiaoDVD.zip:movie-ratings.txt', 'ciaodvd_ratings', None),
+         (('CiaoDVD.zip:review-ratings.txt', 'CiaoDVD.zip:movie-ratings.txt'),
+          'ciaodvd_review_ratings', None)]
+
+def fetch_upstream(force=False):
+    return fetch_upstream_files(__name__.split(".")[-1], upstream_prefix, files,
+                                force)
+
+def _rows(name, fname):
+    with open_upstream_file(name, fname, "r") as f:
+        for line in f:
+            line = line.strip()
+            if line:
+                yield line.replace(",", " ").split()
+
+def _date(x):
+    return calendar.timegm(time.strptime(x, "%Y-%m-%d"))
+
+@cache_network()
+@coerce_props()
+@annotate()
+def parse(alts=None):
+    name = __name__.split(".")[-1]
+    for fnames, alt, fmt in files:
+        if alts is not None and alt not in alts:
+            continue
+        if isinstance(fnames, str):
+            fnames = [fnames]
+        rows = list(_rows(name, fnames[0]))
+        if alt.endswith("_trust"):
+            assert all(r[2] == "1" for r in rows)
+            g = Graph(directed=True)
+            g.vp.user_id = g.add_edge_list([(int(r[0]), int(r[1])) for r in rows],
+                                           hashed=True, hash_type="int")
+        elif alt == "ciaodvd_review_ratings":
+            author = {r[3]: int(r[0]) for r in _rows(name, fnames[1])}
+            g = Graph(directed=True)
+            g.ep.review_id = g.new_ep("int64_t")
+            g.ep.helpfulness = g.new_ep("int32_t")
+            g.vp.user_id = g.add_edge_list([(int(r[0]), author[r[1]], int(r[1]),
+                                             int(r[2])) for r in rows],
+                                           hashed=True, hash_type="int",
+                                           eprops=[g.ep.review_id,
+                                                   g.ep.helpfulness])
+        else:
+            users = sorted(set(int(r[0]) for r in rows))
+            movies = sorted(set(int(r[1]) for r in rows))
+            uidx = {u: i for i, u in enumerate(users)}
+            midx = {m: len(users) + i for i, m in enumerate(movies)}
+            g = Graph(directed=False)
+            g.add_vertex(len(users) + len(movies))
+            g.vp.is_movie = g.new_vp("bool")
+            g.vp.is_movie.a[len(users):] = True
+            g.vp.user_id = g.new_vp("int64_t")
+            g.vp.movie_id = g.new_vp("int64_t")
+            g.vp.user_id.a[:len(users)] = users
+            g.vp.user_id.a[len(users):] = -1
+            g.vp.movie_id.a[:len(users)] = -1
+            g.vp.movie_id.a[len(users):] = movies
+            g.ep.rating = g.new_ep("double")
+            if alt == "filmtrust_ratings":
+                g.add_edge_list([(uidx[int(r[0])], midx[int(r[1])], float(r[2]))
+                                 for r in rows], eprops=[g.ep.rating])
+            else:
+                g.vp.genre = g.new_vp("int32_t", val=-1)
+                for r in rows:
+                    gen = int(r[2])
+                    v = midx[int(r[1])]
+                    assert g.vp.genre[v] in (-1, gen)
+                    g.vp.genre[v] = gen
+                g.ep.review_id = g.new_ep("int64_t")
+                g.ep.time = g.new_ep("int64_t")
+                g.add_edge_list([(uidx[int(r[0])], midx[int(r[1])], float(r[4]),
+                                  int(r[3]), _date(r[5])) for r in rows],
+                                eprops=[g.ep.rating, g.ep.review_id, g.ep.time])
+        yield alt, g