Sequential Attention Source Identification Based on Feature Representation

Dongpeng Hou, Zhen Wang, Chao Gao, Xuelong Li

科研成果: 书/报告/会议事项章节会议稿件同行评审

11 引用 (Scopus)

摘要

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.

源语言英语
主期刊名Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
编辑Edith Elkind
出版商International Joint Conferences on Artificial Intelligence
4794-4802
页数9
ISBN(电子版)9781956792034
DOI
出版状态已出版 - 2023
活动32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 - Macao, 中国
期限: 19 8月 202325 8月 2023

出版系列

姓名IJCAI International Joint Conference on Artificial Intelligence
2023-August
ISSN(印刷版)1045-0823

会议

会议32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
国家/地区中国
Macao
时期19/08/2325/08/23

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