부산대학교 도서관

In this work, we propose a linear continuous time opinion dynamics model that leads to the existence of diverse opinions called ‘clustering’ of opinions in a network at steady state. A clustering vector defines the number of clusters in the steady-state opinions pattern, the nodes belonging to each cluster and the ratio of opinion values of any two clusters. Desired clustering in the network refers to the convergence of opinions preserving the properties of the clustering vector. The clustering of opinions in the multiagent framework has applications ranging from task allocation in autonomous agents to social network analysis. Under the assumption that the network has at least one globally reachable node, We propose the design of a clustering matrix in the opinion model that leads to the desired clustering of opinions in a network with cooperative and antagonistic interactions. We also outline properties of the clustering matrix that are analogous to properties of the graph Laplacian matrix for undirected networks. All the results are verified with simulations.