Peer selection in opinion dynamics on signed social networks with stubborn individuals

Abstract

This paper examines the impact of local peer selection on global opinion dynamics evolving on signed social networks, where both cooperative and antagonistic ties coexist. Specifically, we focus on how opinions of stubborn individuals can be efficiently learned by a signed social network using peer selection mechanism. First, we examine the correlation between the selection of influence anchors of stubborn individuals and the opinion formation of the signed social network. Then, we proceed to examine an eigenvector-based peer selection strategy and show how the strategy can be beneficial in the spreading efficiency of stubborn individuals’ opinions. Finally, how the local observations of peers’ opinions can be employed for peer selection is investigated, enabling a global opinion spreading efficiency enhancement through only local decision-making amongst social individuals. The application of the proposed peer selection strategy to the Friedkin-Johnsen opinion dynamics model on signed networks is discussed. Numerical examples are provided to demonstrate the theoretical results.