{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/graph-neural-networks-for-social","title":"Graph Neural Networks for Social Recommendation","arxiv_id":"1902.07243","date":"2019-02-19","proceeding":null,"authors":["Wenqi Fan","Yao Ma","Qing Li","Yuan He","Eric Zhao","Jiliang Tang","Dawei Yin"],"abstract":"In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec. Our code is available at \\url{https://github.com/wenqifan03/GraphRec-WWW19}","url_abs":"https://arxiv.org/abs/1902.07243v2","url_pdf":"https://arxiv.org/pdf/1902.07243v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"graph-neural-networks-for-social","repo_url":"https://github.com/wenqifan03/GraphRec-WWW19","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"graph-neural-networks-for-social","repo_url":"https://github.com/Lior157/ComGraphRec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"graph-neural-networks-for-social","repo_url":"https://github.com/Wang-Shuo/GraphRec_PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"graph-neural-networks-for-social","repo_url":"https://github.com/YangLiangwei/ConsisRec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"graph-neural-networks-for-social","repo_url":"https://github.com/florianeBhz/GraphRec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"graph-neural-networks-for-social","repo_url":"https://github.com/linhthi/ts","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"graph-neural-networks-for-social","repo_url":"https://github.com/wenqifan03/wenqifan03.github.io","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"graph-neural-networks-for-social","repo_url":"https://github.com/MindSpore-scientific/code-3/tree/main/Analysis-of-Black-Hole-Solutions-in-Parabolic-Class-Using-Neural-Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"graph-neural-networks-for-social","repo_url":"https://github.com/lcwy220/Social-Recommendation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/recommendation-systems-on-epinions","task":"Recommendation Systems","dataset":"Epinions","model":"GraphRec","rank_in_archive_order":3,"of":4,"metrics":{"MAE":"0.8168","RMSE":"1.0631"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.07243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.07243"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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