Cost-Effective Viral Marketing for Time-Critical Campaigns in Large-Scale Social Networks

Online social networks (OSNs) have become one of the most effective channels for marketing and advertising. Since users are often influenced by their friends, “word-of-mouth” exchanges, so-called viral marketing, in social networks can be used to increase product adoption or widely spread content over the network. The common perception of viral marketing about being cheap, easy, and massively effective makes it an ideal replacement of traditional advertising. However, recent studies have revealed that the propagation often fades quickly within only few hops from the sources, counteracting the assumption on the self-perpetuating of influence considered in literature. With only limited influence propagation, is massively reaching customers via viral marketing still affordable? How do we economically spend more resources to increase the spreading speed? We investigate the cost-effective massive viral marketing problem, taking into the consideration the limited influence propagation. Both analytical analysis based on power-law network theory and numerical analysis demonstrate that the viral marketing might involve costly seeding. To minimize the seeding cost, we provide mathematical programming to find optimal seeding for medium-size networks and propose VirAds, an efficient algorithm, to tackle the problem on large-scale networks. VirAds guarantees a relative error bound of O(1) from the optimal solutions in power-law networks and outperforms the greedy heuristics that realizes on the degree centrality. Moreover, we also show that, in general, approximating the optimal seeding within a ratio better than O(logn) is unlikely possible.