Key nodes of misinformation source inference: A message-passing-based approach

Xiaohang Yu, Yanyi Nie, Wenyao Li, Tao Lin, Yu Chen, Feng Gao and Wei Wang
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Xiaohang Yu: College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
Yanyi Nie: College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
Wenyao Li: College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
Tao Lin: College of Computer Science, Sichuan University, Chengdu 610065, P. R. China
Yu Chen: School of Intelligent Science and Technology, Sichuan Minzu College, Kangding 626001, P. R. China
Feng Gao: School of Artificial Intelligence, Chongqing University of Arts and Sciences, Chongqing, 402160, P. R. China
Wei Wang: School of Public Health and Management, Chongqing Medical University, Chongqing 400016, P. R. China

International Journal of Modern Physics C (IJMPC), 2024, vol. 35, issue 11, 1-22

Abstract: The misinformation spreading in social networks causes unpredictable damage to the networked system, thus inferring the misinformation source is an important research topic in the field of network science and security. Many source inference algorithms have been proposed to find the most likely propagation source through observable snapshot. However, under limited observable conditions, observing different nodes states markedly affects the algorithm’s effectiveness. Yet, we still lack relevant research on which nodes can more accurately assist us in completing source inference. Here, we propose the heuristic message-passing-based algorithm to find the key nodes that can maximize the accuracy of source inference, which uses the average rank of the source in the message-passing method as a measure and performs continuous annealing on this basis to update the set. As a comparison, we propose random selection algorithm as the basic, high-eigenvalue algorithm and high-degree algorithm focused on centrality, and basic message-passing-based algorithm from the perspective of energy entropy in message passing. Through extensive numerical simulation on artificial and real-world networks, compared with other four algorithms, our heuristic message-passing-based algorithm finds the optimal key node set that can more accurately complete source inference. Moreover, it has over 8% higher inference accuracy than other methods in low visibility situations especially.

Keywords: Social networks; key nodes; misinformation spreading; source inference; message-passing-based approach (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0129183124501468

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