Least Cost Influence Maximization Across Multiple Social Networks

Abstract— In online social networks(OSNs),the least cost influence (LCI) problem has become one of the central re-search topics. It aims at identifying a minimum number of seed users who can’t rigger a wide cascade of information propagation. Most of existing literature investigated the LCI problem only based on an individual network. However, nowadays users often join several OSNs such that information could be spread across different networks simultaneously. Therefore, in order to obtain the best set of seed users, it is crucial to consider the role of over- aping users under this circumstances. In this article, we propose a unified framework to represent and analyse the influence diffusion in multiplex networks. More specifically, we tackle the LCI problem by mapping a set of networks in to a single one via lossless and lossy coupling schemes. The lossless coupling scheme pre-serves all properties of original networks to achieve high-quality solutions, while the lossy coupling scheme offers an attractive alternative when the running time and memory consumption are of primary concern. Various experiments conducted on both real and synthesized datasets have validated the effectiveness of the coupling schemes, which also provide some interesting insights into the process of influence propagation in multiplex networks. < final year projects >