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Most previous work on influence maximization in social networks is limited tothe non-adaptive setting in which the marketer is supposed to select all of theseed users, to give free samples or discounts to, up front. A disadvantage ofthis setting is that the marketer is forced to select all the seeds basedsolely on a diffusion model. If some of the selected seeds do not perform well,there is no opportunity to course-correct. A more practical setting is theadaptive setting in which the marketer initially selects a batch of users andobserves how well seeding those users leads to a diffusion of productadoptions. Based on this market feedback, she formulates a policy for choosingthe remaining seeds. In this paper, we study adaptive offline strategies fortwo problems: (a) MAXSPREAD -- given a budget on number of seeds and a timehorizon, maximize the spread of influence and (b) MINTSS -- given a timehorizon and an expected number of target users to be influenced, minimize thenumber of seeds that will be required. In particular, we present theoreticalbounds and empirical results for an adaptive strategy and quantify itspractical benefit over the non-adaptive strategy. We evaluate adaptive andnon-adaptive policies on three real data sets. We conclude that while benefitof going adaptive for the MAXSPREAD problem is modest, adaptive policies leadto significant savings for the MINTSS problem.

Adaptive Influence Maximization in Social Networks: Why Commit when You can Adapt?