Competitive influence maximisation model with monetary incentive

Abstract

ABSTRACT The spreading of information in social networks can be modelled as a process of diffusing information with a probability from its source to its neighbours. There is a challenge in the real world where competing companies implement their strategies to gain influence in the same social network at the same time. To effectively control the spreading of processes within the network, the effective use of limited resources is of prime importance. When budgets are fixed, competitors will search for a set of seed members to diffuse influence and maximise the number of members that are affected. Each competitor seeks to maximise its influence by investing in the most influential members in the given social network. In this paper, we utilise the Colonel Blotto game to help competitors figure out how many resources should be allocated to influential nodes to increase the influences on nodes. This is done while also taking into account that competing campaigns are trying to do the same thing. We propose a Max-Influence-Independent-Set (MIIS) algorithm to determine the most influential independent set and find the optimal investment to gain maximum influence in the given social network. The effectiveness of this approach is evaluated under different parameter values, namely probability distributions, topologies, and density.