Managing consensus in balanced networks based on opinion and Trust/Distrust evolutions

Trust and distrust relationships are both common in social interactions of group decision making (GDM). Traditionally, trust/distrust levels are fixed in the opinion evolution process and irrele-vant to the divergence of opinions. Breaking the routine, our paper considers a GDM problem with an intertwined evolution of opinions and trust/distrust levels in a balanced network (BN). To solving this problem, we propose a consensus-reaching framework to characterize the interactive and iterative process of opinion dynamics and trust/distrust level evolution in BNs. From the framework, we obtain the stable results of opinion dynamics and determine their convergence range in different BNs. Meanwhile, despite no consensus formed in these results, the key role of opinion leaders is captured in settling divergences. To eliminate the divergence and promote the consensus-reaching, we then devise leader-based strategies of minimum adjustments corre-sponding to different BNs by building consensus models. Finally, a series of simulation experi-ments are provided to validate the feasibility and effectiveness of our theoretical results, and a comparative analysis is implemented to further verify the effect of our consensus models.