{"type":"ScholarlyArticle","title":"Multi-Relational Graph Attention Network for Social Relationship Inference from Human Mobility Data","authors":[{"name":"Guangming Qin","slug":"guangming-qin","given":"Guangming","family":"Qin","affiliation":"Ocean University of China"},{"name":"Jianpeng Qi","slug":"jianpeng-qi","given":"Jianpeng","family":"Qi","affiliation":"Ocean University of China"},{"name":"Bin Wang","slug":"bin-wang","given":"Bin","family":"Wang","affiliation":"Ocean University of China"},{"name":"Guiyuan Jiang","slug":"guiyuan-jiang","given":"Guiyuan","family":"Jiang","affiliation":"Ocean University of China"},{"name":"Yanwei Yu","slug":"yanwei-yu","given":"Yanwei","family":"Yu","affiliation":"Ocean University of China"},{"name":"Junyu Dong","slug":"junyu-dong","given":"Junyu","family":"Dong","affiliation":"Ocean University of China"}],"abstract":"Inferring social relationships from human mobility data holds significant value in real-life spatio-temporal applications, which inspires the development of a series of graph-based methods for inferring social relationships. Despite their effectiveness, we argue that previous methods either rely solely on direct relations between users, neglecting valuable user mobility patterns, or have not fully harnessed the indirect interactions, thereby struggling to capture users' mobility preferences. To address these issues, in this work, we propose the Multi-Relational Graph Attention Network (MRGAN), a novel graph attention network, which is able to explicitly model indirect relations and effectively capture their different impact. Specifically, we first extract a multi-relational graph from heterogeneous mobility graph to explicitly model the direct and indirect relations,and then utilize influence attention and cross-relation attention to further capture the different influence between users, and different importance of relations for each user. Comprehensive experiments on three real-world mobile datasets demonstrate that the proposed model significantly outperforms state-of-the-art models in predicting social relationships between users. The source code of our model is available at https://github.com/qinguangming1999/MRGAN_IJCAI.","doi":"10.24963/ijcai.2024/256","url":"https://ijcai.org/proceedings/2024/256","pdf":"https://ijcai.org/proceedings/2024/0256.pdf","markdown":"https://ijcai.org/proceedings/2024/256.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/256","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":256,"pages":{"first":2315,"last":2323},"keywords":["Data Mining: DM: Mining spatial and/or temporal data","Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"[Antelmi et al., 2024] Alessia Antelmi, Gennaro Cordasco, Mirko Polato, Vittorio Scarano, Carmine Spagnuolo, and Dingqi Yang. 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