TY - GEN
T1 - A Rapid Source Localization Method in the Early Stage of Large-scale Network Propagation
AU - Wang, Zhen
AU - Hou, Dongpeng
AU - Gao, Chao
AU - Huang, Jiajin
AU - Xuan, Qi
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/4/25
Y1 - 2022/4/25
N2 - Recently, the rapid diffusion of malicious information in online social networks causes great harm to our society. Therefore, it is of great significance to localize diffusion sources as early as possible to stem the spread of malicious information. This paper proposes a novel sensor-based method, called greedy full-order neighbor localization (denoted as GFNL), to solve this problem under a low infection propagation in line with the real world. More specifically, GFNL includes two main components, i.e., the greedy-based sensor deployment strategy (DS) and direction-path-based source estimation strategy (ES). In more detail, to ensure sensors can observe a propagation information as early as possible, a set of sensors is deployed in a network to minimize the geodesic distance (i.e., the distance of the shortest path) between the candidate set and the sensor set based on DS. Then when a fraction of sensors observe a propagation, ES infers the source based on the idea that the distance of the actual propagation path is proportional to the observed time. Compared with some state-of-the-art methods, comprehensive experiments have proved the superiority and robustness of our proposed GFNL.
AB - Recently, the rapid diffusion of malicious information in online social networks causes great harm to our society. Therefore, it is of great significance to localize diffusion sources as early as possible to stem the spread of malicious information. This paper proposes a novel sensor-based method, called greedy full-order neighbor localization (denoted as GFNL), to solve this problem under a low infection propagation in line with the real world. More specifically, GFNL includes two main components, i.e., the greedy-based sensor deployment strategy (DS) and direction-path-based source estimation strategy (ES). In more detail, to ensure sensors can observe a propagation information as early as possible, a set of sensors is deployed in a network to minimize the geodesic distance (i.e., the distance of the shortest path) between the candidate set and the sensor set based on DS. Then when a fraction of sensors observe a propagation, ES infers the source based on the idea that the distance of the actual propagation path is proportional to the observed time. Compared with some state-of-the-art methods, comprehensive experiments have proved the superiority and robustness of our proposed GFNL.
KW - network propagation
KW - social network dynamics
KW - source localization
UR - http://www.scopus.com/inward/record.url?scp=85129319287&partnerID=8YFLogxK
U2 - 10.1145/3485447.3512184
DO - 10.1145/3485447.3512184
M3 - 会议稿件
AN - SCOPUS:85129319287
T3 - WWW 2022 - Proceedings of the ACM Web Conference 2022
SP - 1372
EP - 1380
BT - WWW 2022 - Proceedings of the ACM Web Conference 2022
PB - Association for Computing Machinery, Inc
T2 - 31st ACM World Wide Web Conference, WWW 2022
Y2 - 25 April 2022 through 29 April 2022
ER -