Abstract:
The rapid spread of misinformation in social media presents significant threats to society, highlighting the importance of early inference of the diffusion source to mini...Show MoreMetadata
Abstract:
The rapid spread of misinformation in social media presents significant threats to society, highlighting the importance of early inference of the diffusion source to minimize potential losses. Although sensor-based methods have proven effective in source localization, their reliance on sufficient information from all sensors restricts their ability to accurately identify the source with limited data from a few sensors, thereby limiting their application in early propagation scenarios. To address these challenges, this paper introduces a novel method called random full-order-coverage based rapid source localization (RF-RSL). RF-RSL improves the greedy-based strategy (GS) in a random deployment way to quickly provide extensive coverage of deployed sensors over a wide area, followed by the limited-information-oriented strategy (LS) for source inference with an early response mechanism. Specifically, LS incorporates a quick preprocessing step to eliminate invalid candidates and a novel source estimator for precise source identification. The experiments demonstrate that RF-RSL consistently outperforms the best baseline by at least 5% and exhibits exceptional advantages of up to 30% when deployed with fewer sensors. Moreover, RF-RSL showcases a remarkable speed advantage of over 10 times compared to the best baseline in large-scale networks.
Published in: IEEE Transactions on Network Science and Engineering ( Volume: 11, Issue: 5, Sept.-Oct. 2024)