Competitive Opinion Influence Maximization in Social Networks

Abstract

In today's world, social networks have become one of the most important communication tools where individuals and organizations can exchange information and opinions. This significantly affects social attitudes and behaviors, making it essential to understand the processes of information dissemination in this context. This study addresses the influence maximization problem in competitive opinion diffusion. Unlike prior heuristic approaches, we formulated a bi-level mathematical programming model based on game theory, leveraging a Stackelberg game framework to model leader-follower strategic interactions. The model is solved using a genetic algorithm to identify effective dissemination strategies. Findings show that key parameters - delta threshold, social influence, initial adopters, and transmission cost - significantly affect diffusion. The bi-level model optimizes message dissemination across threshold values, highlighting the role of content attractiveness for user engagement. Lower transmission costs boost participation, increasing active nodes. The involvement of influential users at the outset amplifies dissemination. This research demonstrates that optimizing key parameters and reducing costs improves diffusion strategies, enhancing message impact. These results generalize to similar networks and have practical marketing applications.