Least Cost Influence Maximization Across Multiple Social Networks

TL;DR

This paper addresses the Least Cost Influence maximization problem across multiple social networks by proposing a unified framework that considers overlapping users and introduces coupling schemes to efficiently analyze influence diffusion in multiplex networks.

Contribution

It introduces a novel unified framework with lossless and lossy coupling schemes for influence maximization across multiplex networks, considering overlapping users.

Findings

Lossless coupling preserves network properties for high-quality solutions.

Lossy coupling reduces computational resources with acceptable accuracy.

Experimental results validate the effectiveness of the proposed schemes.

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.