Dynamic Propagation Rates: New Dimension to Viral Marketing in Online Social Networks

Abstract

Online Social Networks (OSNs) are effective platforms for viral marketing. Due to their importance, viral marketing related problems in OSNs have been extensively studied in the past decade. However, none of the existing works can cope with the situation that the propagation rate dynamically increases for popular topics, as they all assume known propagation rates. In this paper, to better describe realistic information propagation in OSNs, we propose a novel model, Dynamic Influence Propagation (DIP), that allows propagation rate to change during the diffusion. We then define a new research problem: Threshold Activation Problem under DIP (TAP-DIP) to study the impact of DIP. TAP-DIP adds extra complexity on the already #P-hard TAP problem. Despite it hardness, we are able to approximate TAP-DIP with O(log|V|) ratio. Sitting in the core of our algorithm are the Lipschitz optimization technique and a novel solution to the general version of TAP, the Multi-TAP problem. Using various real OSN datasets, we experimentally demonstrate the impact of DIP and that our solution not only generates high-quality seed sets when being aware of the rate increase, but also is scalable.