{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:14:30Z","timestamp":1783700070671,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T00:00:00Z","timestamp":1663632000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Future Network Scientific Research Fund Project","award":["FNSRFP-2021-YB-54"],"award-info":[{"award-number":["FNSRFP-2021-YB-54"]}]},{"name":"Future Network Scientific Research Fund Project","award":["17KJB520028"],"award-info":[{"award-number":["17KJB520028"]}]},{"name":"Future Network Scientific Research Fund Project","award":["XK203XZ21001"],"award-info":[{"award-number":["XK203XZ21001"]}]},{"name":"Natural Science Foundation of the Higher Education Institutions of Jiangsu Province","award":["FNSRFP-2021-YB-54"],"award-info":[{"award-number":["FNSRFP-2021-YB-54"]}]},{"name":"Natural Science Foundation of the Higher Education Institutions of Jiangsu Province","award":["17KJB520028"],"award-info":[{"award-number":["17KJB520028"]}]},{"name":"Natural Science Foundation of the Higher Education Institutions of Jiangsu Province","award":["XK203XZ21001"],"award-info":[{"award-number":["XK203XZ21001"]}]},{"name":"Tongda College of Nanjing University of Posts and Telecommunications","award":["FNSRFP-2021-YB-54"],"award-info":[{"award-number":["FNSRFP-2021-YB-54"]}]},{"name":"Tongda College of Nanjing University of Posts and Telecommunications","award":["17KJB520028"],"award-info":[{"award-number":["17KJB520028"]}]},{"name":"Tongda College of Nanjing University of Posts and Telecommunications","award":["XK203XZ21001"],"award-info":[{"award-number":["XK203XZ21001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Social-network-based recommendation algorithms leverage rich social network information to alleviate the problem of data sparsity and boost the recommendation performance. However, traditional social-network-based recommendation algorithms ignore high-order collaborative signals or only consider the first-order collaborative signal when learning users\u2019 and items\u2019 latent representations, resulting in suboptimal recommendation performance. In this paper, we propose a graph neural network (GNN)-based social recommendation model that utilizes the GNN framework to capture high-order collaborative signals in the process of learning the latent representations of users and items. Specifically, we formulate the representations of entities, i.e., users and items, by stacking multiple embedding propagation layers to recursively aggregate multi-hop neighborhood information on both the user\u2013item interaction graph and the social network graph. Hence, the collaborative signals hidden in both the user\u2013item interaction graph and the social network graph are explicitly injected into the final representations of entities. Moreover, we ease the training process of the proposed GNN-based social recommendation model and alleviate overfitting by adopting a lightweight GNN framework that only retains the neighborhood aggregation component and abandons the feature transformation and nonlinear activation components. The experimental results on two real-world datasets show that our proposed GNN-based social recommendation method outperforms the state-of-the-art recommendation algorithms.<\/jats:p>","DOI":"10.3390\/s22197122","type":"journal-article","created":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T00:08:09Z","timestamp":1663718889000},"page":"7122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A Graph-Neural-Network-Based Social Network Recommendation Algorithm Using High-Order Neighbor Information"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2587-8090","authenticated-orcid":false,"given":"Yonghong","family":"Yu","sequence":"first","affiliation":[{"name":"College of Tongda, Nanjing University of Posts and Telecommunication, Yangzhou 225127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2523-8173","authenticated-orcid":false,"given":"Weiwen","family":"Qian","sequence":"additional","affiliation":[{"name":"College of Tongda, Nanjing University of Posts and Telecommunication, Yangzhou 225127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6674-692X","authenticated-orcid":false,"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Royal Holloway, University of London, Egham TW20 0EX, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions","volume":"17","author":"Adomavicius","year":"2005","journal-title":"IEEE Trans. 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