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Predictive modeling of complex network structure and dynamics using machine learning methods

Abstract

Complex networks serve as a representation of the intricate structures found in complex systems, ranging from biological interactions to social connections. The study of these networks has gained significant importance in recent decades, enabling the modelling of organizational structures and dynamic processes in various systems. Over the past years, there has been a growing interest in applying machine learning methods to identify patterns of connections within networks and predict dynamic processes. Notably, research has shown that analyzing the structure of a network allows us to infer the outbreak size and the level of synchronization of networks. Building on these investigations, we have collaborated with the Institute for Biocomputation and Physics of Complex Systems at the Universidad de Zaragoza. To strengthen this collaboration, we have invited Professor Yamir Moreno to visit the Complex Systems group at the Institute of Mathematics and Computation (ICMC-USP). The current project outlines the key activities to be developed during Professor Yamir Moreno's visit to ICMC-USP. The objectives include completing ongoing works and exploring new projects through in-depth discussions and knowledge exchange. (AU)

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