Influence Maximisation

Influence maximization (IM) focuses on identifying key individuals or nodes within a network to maximize the spread of information or influence. Current research emphasizes efficient algorithms, including those leveraging Bayesian optimization, reinforcement learning, and quantum-inspired approaches, to address the computational complexity of this problem, particularly in multiplex networks (where individuals participate in multiple interconnected networks) and scenarios requiring fairness in information dissemination. These advancements have significant implications for targeted advertising, viral marketing, public health campaigns, and understanding information diffusion dynamics in complex social systems.