Viral marketing 2.0

Abstract

Over the last decade, there has been considerable excitement and research on the study and exploitation of the spread of information and influence over networks. Tremendous advances have been made on the prototypical problem of selecting a small number of seed users to activate over a social network such that the number of activated nodes in an expected sense is maximized, under several standard information diffusion models. Scalable heuristics, but more notably scalable approximation algorithms, have been developed in the recent years. Unfortunately, the state of the art has several shortcomings. Firstly, most of the research has focused on a simplistic setting where one marketing campaign is active at a time. While there has been some work on modeling and optimizing for competing diffusions, the key role played by the network owner in a campaign has been overlooked. Secondly, the relationship and contract needed between the network owner and the advertisers is not captured. Thirdly, in real life, relationships between multiple campaigns may be more complex than just pure competition. Finally, most of the studies assume that the seeds must be chosen all at once before the campaign starts with no opportunity to observe the performance of seeds chosen earlier and course-correct as needed. We make a call to arms for opening up the framework of viral marketing to allow for more expressive business models and seed selection strategies, and present some recent research from our group that addresses the modeling and computational challenges.