Competitive and Collaborative Influence in Social Networks

Abstract

We consider revenue maximization in viral marketing of competitive and collaborative products through social networks, focusing on the word-of-mouth effect on personal decisions in adopting products, technologies, or Internet applications. In our model, each advertiser submits its value per consumer, and its total budget. The publisher pays a selected set of users on social networks as seed nodes. It demands a payment (equal to its value) from an advertiser for each influenced node in social networks. The publisher’s revenue equals to the total payment from the influenced users minus its cost of seed nodes. In this paper, we study the efficient allocation problem of the publisher to maximize its revenue. Our work is motivated by recent extensive studies on influence models for social networks. It has been noted that the promoted products could either be competitive or complementary such as Kindle versus Nook e-reader or Kindle cover with Kindle, respectively. Our models evaluate the revenue/cost effect of marketing those types of products through a social network, focusing on the issues of revenue maximization and complementary and competitive effects of products on each other. We take the algorithmic complexity approach for revenue maximization under those models and prove NP-hardness, non-approximability results for general structures, and polynomial time algorithms and applications for special classes of networks.