The communication structures within our society can be visualized as networks, that are dynamic and change over time due to various factors. Numerous mathematical models have been developed to simulate opinion dynamics. These models are predominantly agent-based, where an opinion-forming process occurs through interactions between individual agents.
The interaction processes are based on an underlying network of agents. The DeGroot (DG) model is the most well-known continuous opinion space model. According to this model, a person's opinion is derived from their previous opinion and the influence process. In addition to DG-based models , there are also continuous opinion space models with bounded confidence. These are characterized by individuals ignoring ideas or opinions that are too far removed from their own. However, these models do not assume an underlying interaction network, but rather assume interactions between all individuals in the population. We extend the DeGroot-Friedkin (DGF) model with constant self-weights for the development of social influence networks to a bounded confidence model. Based on this extended DGF model, we analyze opinion-forming processes in different network topologies.