The field of opinion dynamics and social network influence is moving towards a deeper understanding of the complex interactions between individuals and their environment. Researchers are exploring new models and techniques to analyze and control the spread of opinions and influence within social networks. A key area of focus is the development of more sophisticated models that incorporate factors such as memory effects, higher-order neighbors, and coevolving actions and opinions. These advancements have the potential to significantly improve our understanding of social phenomena and enable more effective strategies for shaping public opinion.

Noteworthy papers in this area include: Leveraging Network Topology in a Two-way Competition for Influence in the Friedkin-Johnsen Model, which demonstrates how network topology can be used to increase the influence of a preferred stubborn agent. FJ-MM: The Friedkin-Johnsen Opinion Dynamics Model with Memory and Higher-Order Neighbors, which introduces a new model that incorporates memory effects and multi-hop influence, allowing for a more nuanced understanding of opinion dynamics. Controlling a Social Network of Individuals with Coevolving Actions and Opinions, which formulates a control problem for social networks and provides a methodology for steering a population towards a desired consensus state.