  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.
