In recent years social networking platforms have developed into extraordinary channels for spreading and consuming information.
Along with the rise of such infrastructure, there is continuous progress on techniques for spreading information effectively through influential users.  
  
In this paper, we describe scalable algorithms for a new method of information diffusion called adaptive seeding.  In many applications, one is restricted to select influencers from a set of users who engaged with the topic being promoted, and due to the structure of social networks, these users often rank low in terms of their influence potential.  To overcome this hurdle, adaptive seeding aims to select users in a manner which targets their influential neighbors.

Despite the various complexities involved with the optimization problem, we show that scalable adaptive seeding is achievable.  In particular, we develop algorithms for linear influence models with provable approximation guarantees that can be gracefully parallelized.  To show the effectiveness of our methods we collected data from various verticals social network users follow.  For each vertical, we collected data on the users who responded to a certain post as well as their neighbors, and applied our methods on this data.  Our experiments show that adaptive seeding is  scalable, and importantly, that it obtains dramatic improvements over standard approaches of information dissemination.
