Adaptive Influence Maximization

Information diffusion and social influence are more and more present in today's Web ecosystem. Having algorithms that optimize the presence and message diffusion on social media is indeed crucial to all actors (media companies, political parties, corporations, etc.) who advertise on the Web. Motivated by the need for effective viral marketing strategies, influence estimation and influence maximization have therefore become important research problems, leading to a plethora of methods. However, the majority of these methods are non-adaptive, and therefore not appropriate for scenarios in which influence campaigns may be ran and observed over multiple rounds, nor for scenarios which cannot assume full knowledge over the diffusion networks and the ways information spreads in them. In this tutorial we intend to present the recent research on adaptive influence maximization,which aims to address these limitations. This can be seen as a particular case of the influence maximization problem (seeds in a social graph are selected to maximize information spread), one in which the decisions are taken as the influence campaign unfolds, over multiple rounds, and where knowledge about the graph topology and the influence process may be partial or even entirely missing. This setting, depending on the underlying assumptions, leads to variate and original approaches and algorithmic techniques, as we have witnessed in recent literature. We will review the most relevant research in this area, by organizing it along several key dimensions, and by discussing the methods' advantages and shortcomings, along with open research questions and the practical aspects of their implementation. Tutorial slides will become publicly available on https://sites.google.com/view/aim-tutorial/home.