TY - JOUR
T1 - Efficient Source Detection in Incomplete Networks via Sensor Deployment and Source Approaching
AU - Cheng, Le
AU - Zhu, Peican
AU - Tang, Keke
AU - Gao, Chao
AU - Wang, Zhen
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Rumor source detection in structurally incomplete networks holds significant practical importance. Existing methods predominantly assume a complete network structure information; furthermore, they often neglect the issue of resource consumption, i.e., sensor deployment. In this paper, we propose an efficient source detection approach in incomplete networks via propagation-aware Sensor Deployment and time stamp-guided Source Approaching (SDSA) to tackle these challenges. Specifically, during the sensor deployment phase, we employ quality-guaranteed Monte Carlo propagation simulations coupled with a greedy strategy to achieve maximum coverage with minimal sensors. In the source detection phase, for the structurally incomplete network snapshots, we first attempt edge reconnection from the sensor with the earliest timestamp, followed by posterior maximization Bayesian estimation for source identification. Extensive experiments demonstrate the effectiveness of SDSA and its superiority over state-of-the-art methods. The code has been made publicly available at https://github.com/cheng-le/SDSA.
AB - Rumor source detection in structurally incomplete networks holds significant practical importance. Existing methods predominantly assume a complete network structure information; furthermore, they often neglect the issue of resource consumption, i.e., sensor deployment. In this paper, we propose an efficient source detection approach in incomplete networks via propagation-aware Sensor Deployment and time stamp-guided Source Approaching (SDSA) to tackle these challenges. Specifically, during the sensor deployment phase, we employ quality-guaranteed Monte Carlo propagation simulations coupled with a greedy strategy to achieve maximum coverage with minimal sensors. In the source detection phase, for the structurally incomplete network snapshots, we first attempt edge reconnection from the sensor with the earliest timestamp, followed by posterior maximization Bayesian estimation for source identification. Extensive experiments demonstrate the effectiveness of SDSA and its superiority over state-of-the-art methods. The code has been made publicly available at https://github.com/cheng-le/SDSA.
KW - Incomplete Network
KW - Minimal Sensors
KW - Propagation Dynamics
KW - Rumor Source Detection
KW - Sensor Deployment
UR - http://www.scopus.com/inward/record.url?scp=86000542014&partnerID=8YFLogxK
U2 - 10.1109/TIFS.2025.3550069
DO - 10.1109/TIFS.2025.3550069
M3 - 文章
AN - SCOPUS:86000542014
SN - 1556-6013
JO - IEEE Transactions on Information Forensics and Security
JF - IEEE Transactions on Information Forensics and Security
ER -