A two-step communication opinion dynamics model with self-persistence and influence index for social networks based on the DeGroot model

Existing opinion dynamics models often fail to consider the relationship between one agent and people two degrees of separation from the agent. In addition, no accurate weight determination method has yet been fully developed. To address these limitations, this paper proposes a two-step communication opinion dynamics model based on the classical DeGroot model. Self-persistence is introduced to measure the individual’s adherence to the initial opinion, and a new weight determination method proposed that instead of distributing the weights evenly, defines an influence index that is calculated from the self-persistence and node degrees. To guide public opinion using the proposed opinion dynamics model, an optimization model is established by assuming a connected network; however, when the given network is not connected, a subnetwork recognition algorithm is developed and an edge adding algorithm is proposed to alter the structure. Three opinion control strategies are then used to control the opinion formation process. Numerical examples are given to verify the flexibility and practicality of the proposed opinion control strategies, and extensive simulations over random ER networks are given to provide insights into the parameter behavior of the three strategies.