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Recomm. Syst."],"published-print":{"date-parts":[[2026,12,31]]},"abstract":"<jats:p>\n                    Recent work on fairness-aware graph collaborative filtering (GCF) has shown the effectiveness of graph augmentation as a post-processing strategy for mitigating consumer unfairness. However, most studies remain confined to binary fairness setups and operate under limited experimental conditions, often relying on sparse and small-scale datasets. In this article, we extend our fairness-aware augmentation method to address intersectional unfairness across demographic subgroups, a setting where the intersection of multiple sensitive attributes leads to fine-grained subgroups. To this end, we reformulate the fairness objective to incorporate intersectional demographic groups and evaluate our extended method across interaction configurations that vary in density and scale. Our results reveal that the effectiveness of fair graph augmentations is model-dependent and sensitive to dataset properties. We show that the edges selected during augmentation tend to concentrate around interpretable structural patterns driven by the connected nodes\u2019 characteristics. Furthermore, analyzing how these augmented edges differ across graph-level attributes offers actionable insights into the potential benefits of fairness-oriented graph modifications. Finally, we compare our method with recent fairness-aware baselines, explore the impact of augmenting different graph regions, and assess our mitigation strategy under scenarios with minimal unfairness. Source code publicly available at:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/jackmedda\/Intersectional-FA4GCF\">https:\/\/github.com\/jackmedda\/Intersectional-FA4GCF<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3798097","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T11:34:55Z","timestamp":1771587295000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Graph Augmentation for Intersectional Unfairness Mitigation: A Study across Dataset Scales and Interaction Densities"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6053-3015","authenticated-orcid":false,"given":"Ludovico","family":"Boratto","sequence":"first","affiliation":[{"name":"Universit\u00e0 degli Studi di Cagliari","place":["Cagliari, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9631-1799","authenticated-orcid":false,"given":"Francesco","family":"Fabbri","sequence":"additional","affiliation":[{"name":"Spotify AB","place":["Barcelona, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4668-2476","authenticated-orcid":false,"given":"Gianni","family":"Fenu","sequence":"additional","affiliation":[{"name":"University of Cagliari Department of Mathematics and Computer Science","place":["Cagliari, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1989-6057","authenticated-orcid":false,"given":"Mirko","family":"Marras","sequence":"additional","affiliation":[{"name":"University of Cagliari Department of Mathematics and Computer Science","place":["Cagliari, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1300-1876","authenticated-orcid":false,"given":"Giacomo","family":"Medda","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Cagliari","place":["Cagliari, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","unstructured":"Vito Walter Anelli Yashar Deldjoo Tommaso Di Noia Daniele Malitesta Vincenzo Paparella and Claudio Pomo. 2023. 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