Topic-based targeted influence maximization

Social Networks play a very important role as a medium to propagate information among people. Marketers use this to campaign for their products and influence customers. However, it is not practically possible for a marketer to reach out to each and every individual prospective/existing customer due to the sheer size of the networks (in the orders of millions or billions). Therefore, marketers reach out to a small set of people (influencers) who have the potential to further influence/reach out to the targeted customers. Practically, it is not just enough if these influencers have a large following, they also need to have to be able to influence people in the topic that is relevant to the marketer and the influencer must be able to address the target segment that the marketer is targeting. In this paper, we first analyze various edge weighting mechanisms to incorporate influencing probability and utilize this to propose an algorithm to find influencers to maximize the spread to a specified set of targets.