Detecção de propagadores influentes por aprendizado de máquina

Abstract

The study of complex networks is essential to understanding interconnected systems in areas such as biology, sociology and technology. These networks, made up of nodes and edges, reveal important emerging patterns. Network analysis allows you to optimize interventions and improve systems resilience. The identification of influential propagators in a network, such as those responsible for the spread of diseases or information, is fundamental and depends on the dynamics of the phenomenon. Different approaches such as k-shell analysis and optimization models help predict these propagators. This study seeks to analyze the relationship between centrality measures and the propagation capacity in social and spatial networks, proposing prediction models to identify the main influencers and optimize dissemination control.