Presentation Information
[B-7-05]Diffusion-Coefficient and Degree Conditions for Fake News Mitigation on Extended-Cycle Networks
◎△Yasutaka Sato1, Kazuki Nakajima1, Masaki Aida1 (1. Tokyo Metropolitan Univ.)
Keywords:
fake news,activator-inhibitor model,node degree,extended cycle
There have been reports that issuing corrective information as a countermeasure against the spread of fake news on social media can sometimes worsen the situation. To address this issue, theoretical studies based on an activator–inhibitor model have been conducted to investigate effective strategies for disseminating corrective information without amplifying misinformation.
In this model, individuals are represented as nodes in a network, and both fake news and corrective information spread through links between nodes. In this study, we develop a theoretical framework based on extended cycles to analyze the effect of node degree when fake news and corrective information propagate through distinct network structures.
An extended cycle is a (2r)-regular graph obtained by connecting each node in a cycle graph to its (r) nearest neighbors on both sides. Because regular graphs with different node degrees can be generated while keeping the number of nodes fixed, extended cycles provide a useful framework for investigating the influence of node degree on a common set of nodes.
In this model, individuals are represented as nodes in a network, and both fake news and corrective information spread through links between nodes. In this study, we develop a theoretical framework based on extended cycles to analyze the effect of node degree when fake news and corrective information propagate through distinct network structures.
An extended cycle is a (2r)-regular graph obtained by connecting each node in a cycle graph to its (r) nearest neighbors on both sides. Because regular graphs with different node degrees can be generated while keeping the number of nodes fixed, extended cycles provide a useful framework for investigating the influence of node degree on a common set of nodes.