Unveiling Agents' Confidence in Opinion Dynamics Models via Graph Neural Networks Vargas Pérez, Víctor Giráldez Cru, Jesús Mesejo Santiago, Pablo Cordón García, Óscar Agent-based modeling Bounded confidence Graph neural networks Hegselmann–Krause model Opinion dynamics Opinion Dynamics models in social networks are a valuable tool to study how opinions evolve within a population. However, these models often rely on agent-level parameters that are difficult to measure in a real population. This is the case of the confidence threshold in opinion dynamics models based on bounded confidence, where agents are only influenced by other agents having a similar opinion (given by this confidence threshold). Consequently, a common practice is to apply a universal threshold to the entire population and calibrate its value to match observed real-world data, despite being an unrealistic assumption. In this work, we propose an alternative approach using graph neural networks to infer agent-level confidence thresholds in the opinion dynamics of the Hegselmann-Krause model of bounded confidence. This eliminates the need for additional simulations when faced with new case studies. To this end, we construct a comprehensive synthetic training dataset that includes different network topologies and configurations of thresholds and opinions. Through multiple training runs utilizing different architectures, we identify GraphSAGE as the most effective solution, achieving a coefficient of determination R² above 0.7 in test datasets derived from real-world topologies. Remarkably, this performance holds even when the test topologies differ in size from those considered during training. 2024-12-17T13:35:13Z 2024-12-17T13:35:13Z 2024-12-11 journal article Vargas-Pérez, V. A., Giráldez-Cru, J., Mesejo, P., & Cordón, O. (2024). Unveiling Agents’ Confidence in Opinion Dynamics Models via Graph Neural Networks. IEEE Transactions on Computational Social Systems. https://hdl.handle.net/10481/98148 10.1109/TCSS.2024.3508452 eng http://creativecommons.org/licenses/by/4.0/ open access Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Atribución 4.0 Internacional