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LEAST COST INFLUENCE MAXIMIZATION ACROSS MULTIPLE SOCIAL
NETWORKS
ABSTRACT:
Recently, in online social networks (OSNs), the least cost influence (LCI) problem
has become one of the central research topics. It aims at identifying a minimum number of
seed users who can trigger 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 overlapping users under this circumstances. In this article, we
propose a unified framework to represent and analyze the influence diffusion in multiplex
networks. More specifically, we tackle the LCI problem by mapping a set of networks into a
single one via lossless and lossy coupling schemes. The lossless coupling scheme preserves
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.