False information sources detecting based on an epidemic diffusion model

Abstract

Epidemic-based diffusion models are commonly used to locate false information sources. However, most existing models overlook the topological structure of social networks, assuming that each infected person has an equal opportunity to contact all healthy individuals. Additionally, most current source locating algorithms only consider information from infected nodes, neglecting uninfected ones. As a result, these methods often produce unsatisfactory multi-source detection results. To address these shortcomings, we propose a new epidemic diffusion model, Networked-SNIR, which incorporates topological information to more accurately describe influence propagation. We analyze the properties of information propagation under the Networked-SNIR model. To reduce computational time for extensive information propagation simulations, we present an efficient algorithm to estimate the likelihood of nodes being in different states and their infection times. We also propose a likelihood maximization-based algorithm to detect multiple sources of false information. Experimental results on real-world data show that the proposed Networked-SNIR model more accurately reflects the spread of infectious diseases in social networks compared to other models. Furthermore, experiments on seven real-world and two synthetic datasets demonstrate that, compared to baseline algorithms, the sources detected by our algorithm not only influence more observed nodes but also do so at more precise times.