Incentivized Campaigning in Social Networks

TL;DR

This paper develops a theoretical framework using percolation and reliability theory to optimize incentivized campaigns in social networks, balancing cost and reach, with algorithms validated through real-world network simulations.

Contribution

It introduces a novel optimization approach for incentivized social campaigns using fixed point analysis and reliability theory, enabling efficient solutions for cost and reach maximization.

Findings

Algorithms are linearithmic in maximum node degree.

Analytical solutions effectively predict campaign reach and cost.

Simulations on real networks validate the approach.

Abstract

Campaigners, advertisers and activists are increasingly turning to social recommendation mechanisms, provided by social media, for promoting their products, services, brands and even ideas. However, many times, such social network based campaigns perform poorly in practice because the intensity of the recommendations drastically reduces beyond a few hops from the source. A natural strategy for maintaining the intensity is to provide incentives. In this paper, we address the problem of minimizing the cost incurred by the campaigner for incentivizing a fraction of individuals in the social network, while ensuring that the campaign message reaches a given expected fraction of individuals. We also address the dual problem of maximizing the campaign penetration for a resource constrained campaigner. To help us understand and solve the above mentioned problems, we use percolation theory to formally state them as optimization problems. These problems are not amenable to traditional approaches because of a fixed point equation that needs to be solved numerically. However, we use results from reliability theory to establish some key properties of the fixed point, which in turn enables us to solve these problems using algorithms that are linearithmic in maximum node degree. Furthermore, we evaluate the efficacy of the analytical solution by performing simulations on real world networks.