PROJECT TITLE :

Least Cost Influence Maximization Across Multiple Social Networks

ABSTRACT:

Recently, in on-line social networks (OSNs), the least price influence (LCI) drawback has become one in all the central research topics. It aims at identifying a minimum variety of seed users who can trigger a wide cascade of data propagation. Most of existing literature investigated the LCI drawback solely primarily based on an individual network. But, today users usually be a part of many OSNs such that information might be spread across completely different networks simultaneously. So, in order to obtain the simplest set of seed users, it's crucial to consider the role of overlapping users underneath this circumstances. In this text, we tend to propose a unified framework to represent and analyze the influence diffusion in multiplex networks. Additional specifically, we tend to tackle the LCI problem by mapping a collection of networks into one one via lossless and lossy coupling schemes. The lossless coupling theme preserves all properties of original networks to achieve high-quality solutions, whereas the lossy coupling scheme offers an engaging different when the running time and memory consumption are of primary concern. Various experiments conducted on each real and synthesized datasets have validated the effectiveness of the coupling schemes, which conjointly offer some interesting insights into the method of influence propagation in multiplex networks.