{"type":"ScholarlyArticle","title":"Sequential Attention Source Identification Based on Feature Representation","authors":[{"name":"Dongpeng Hou","slug":"dongpeng-hou","given":"Dongpeng","family":"Hou","affiliation":"School of Mechanical Engineering , Northwestern Polytechnical University (NWPU); School of Artificial Intelligence, Optics and Electronics (iOPEN) , Northwestern Polytechnical University (NWPU)"},{"name":"Zhen Wang","slug":"zhen-wang","given":"Zhen","family":"Wang","affiliation":"School of Mechanical Engineering , Northwestern Polytechnical University (NWPU); School of Artificial Intelligence, Optics and Electronics (iOPEN) , Northwestern Polytechnical University (NWPU)"},{"name":"Chao Gao","slug":"chao-gao","given":"Chao","family":"Gao","affiliation":"School of Artificial Intelligence, Optics and Electronics (iOPEN) , Northwestern Polytechnical University (NWPU)"},{"name":"Xuelong Li","slug":"xuelong-li","given":"Xuelong","family":"Li","affiliation":"School of Artificial Intelligence, Optics and Electronics (iOPEN) , Northwestern Polytechnical University (NWPU)"}],"abstract":"Snapshot observation based source localization has been widely studied due to its accessibility and low cost. However, the interaction of users in existing methods does not be addressed in time-varying infection scenarios. So these methods have a decreased accuracy in heterogeneous interaction scenarios. To solve this critical issue, this paper proposes a sequence-to-sequence based localization framework called Temporal-sequence based Graph Attention Source Identification (TGASI) based on an inductive learning idea. More specifically, the encoder focuses on generating multiple features by estimating the influence probability between two users, and the decoder distinguishes the importance of prediction sources in different timestamps by a designed temporal attention mechanism. It's worth mentioning that the inductive learning idea ensures that TGASI can detect the sources in new scenarios without knowing other prior knowledge, which proves the scalability of TGASI. Comprehensive experiments with the SOTA methods demonstrate the higher detection performance and scalability in different scenarios of TGASI.","doi":"10.24963/ijcai.2023/533","url":"https://ijcai.org/proceedings/2023/533","pdf":"https://ijcai.org/proceedings/2023/0533.pdf","markdown":"https://ijcai.org/proceedings/2023/533.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/533","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":533,"pages":{"first":4794,"last":4802},"keywords":["Data Mining: DM: Networks","Machine Learning: ML: Sequence and graph learning","Multidisciplinary Topics and Applications: MDA: Web and social networks"],"videos":[],"references":[{"position":1,"raw":"[Agarwal et al., 2021] Nidhi Agarwal, Akanksha Sondhi, Khyati Chopra, and Ghanapriya Singh. 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