Study on the Collective Behaviors of Topologically Interacting Neighbors Using Network Theory

In recent years, consensus dynamics and artificial intelligence have attracted attention in studying modern social units such as intelligent grid communities. Research in consensus dynamics involves investigating the diverse behaviors of entities, such as the communication between communities and the geometries of social units. When designing optimized social networks, it is essential to consider all situations a social unit may encounter and identify potential issues in advance. Using consensus dynamics in graph theory, this paper investigates the impact of the geometry of social networks on time scales and the consensus of communities composed of multiple decision-making agents The present study explored the adjacency matrix of a structured network with random groups to examine their interactions in this diffusively multi-coupled agent system and elicit a time-scale separation. Computer simulation and calculations utilizing MATLAB and Python programming showed that increased communication and contact might help achieve consensus, but this is dependent on the geometrical dependencies of social structures.