Influence Maximization in Bandit and Adaptive Settings (DRAFT: August 27, 2015)

The objective of viral marketing is to leverage a social network to spread awareness about a specific product in the market through information propagation via word-of-mouth. A closely related problem is that of influence maximization which aims to identify the ‘best’ set of ‘influential’ users (seeds) to give discounts or free products to, such that awareness about the product is maximized. We study two relatively unexplored variants of influence maximization (IM) in social networks. Conventional IM algorithms assume the structure of the social network and edge weights to be known. Though the structure of the network is usually known, edge weights need to be estimated from past data. In the first part of this thesis, we tackle the real but difficult problems of (i) learning these edge weights online and (ii) maximizing influence in the network when no past data is available as input. We adopt a combinatorial multiarmed bandit (CMAB) paradigm and formulate the above problems respectively as (i) network exploration, i.e. incrementally minimizing the error in learned edge weights and (ii) regret minimization i.e. minimizing the loss in influence from choosing suboptimal seed sets over multiple attempts. Most previous work on influence maximization in social networks is limited to the non-adaptive setting in which the marketer is supposed to select all of the seed users up front. A disadvantage of this setting is that she is forced to select all the seeds based solely on a diffusion model. If the selected seeds do not perform well, there is no opportunity to course-correct. A more practical setting is the adaptive setting in which the marketer initially selects a batch of users and observes how seeding those users leads to a diffusion of product adoptions. Based on this market feedback, she formulates a policy for choosing the remaining seeds. We study adaptive offline strategies for two problems: (a) MAXSPREAD given a budget on number of seeds and a time horizon, maximize the spread of influence and (b) MINTSS given a time horizon and an expected number of target users, minimize the number of required seeds.