Cognitive Analysis in Social Networks for Viral Marketing

Viral Marketing can be considered as the modern version of the ancient "word-of-mouth" advertising, in which companies choose a restricted number of persons, considered "influential", recommending them products or services that will be thus iteratively suggested. In this paper, we propose cognitive models and algorithms for marketing applications through Online Social Networks, considered as a graph database, and define the concept of influence graph leveraging particular user behavioral patterns, by querying the initial heterogeneous graph network. We also model the diffusion across the network, without any preliminary information, as a Combinatorial Multi-Armed Bandit problem, for the selection of most influential users. We have used the YELP social network as case study for our approach, showing how it is possible to generate an influence graph considering several kinds of relevant paths (mainly considering reviews to the same firms) by which a user can influence other ones. Several experiments have been carried out and discussed, putting into evidence the effectiveness and efficacy of the proposed methods for influence maximization with respect to other approaches of state of the art.