Influence Diffusion in Online Social Networks With Propagation Rate Changes

Abstract

Information can propagate among Online Social Network (OSN) users at a high speed, which makes the OSNs important platforms for viral marketing. Although the viral marketing related problems in OSNs have been extensively studied in the past decade, the existing works all assume known propagation rates. In this paper, we propose a novel model, Dynamic Influence Propagation (DIP), which allows propagation rates to increase after a topic becomes popular and can be used for describing information propagation in OSNs more realistically. Based on DIP, we define a new research problem: Threshold Activation Problem under DIP (TAP-DIP). However, it adds another layer of complexity over the already #P-hard TAP problem. Despite it hardness, we are able to approximate TAP-DIP with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\log |V|)$</tex-math></inline-formula> ratio, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$|V|$</tex-math></inline-formula> is the number of users in the network. Our solution consists of global optimization techniques and a novel solution to the general version of TAP. We also consider the more complicated case when the propagation rates may change multiple times and the changes are non-immediate, with corresponding solution and analyses. We test our solution using various real OSN datasets, and demonstrate that our solution not only generates high-quality seed sets, but also scales.