Relative influence maximization in competitive dynamics on complex networks | IEEE Conference Publication | IEEE Xplore

Relative influence maximization in competitive dynamics on complex networks


Abstract:

We consider a model of competition on complex networks in which two competitors have fixed and opposite states, and other agents, called normal agents, adjust their state...Show More

Abstract:

We consider a model of competition on complex networks in which two competitors have fixed and opposite states, and other agents, called normal agents, adjust their states according to a distributed consensus protocol. Suppose that one of the competitors could add a given number of new links to enhance his influence which can be defined as the total supporting degree of the normal agents or the number of supporters. We investigate the problem of how to add these links so as to maximize the influence of the competitor over the other one (called relative influence). Under different definitions of influence, two relative influence maximization problems, named Problem 1 and Problem 2, are proposed. We show that the objective function of Problem 1 is monotonous and sub-modular. Hence, there exists a polynomial-time greedy algorithm approximately solving Problem 1. For Problem 2, we prove that it is NP-hard. The Alternative Direction Method of Multipliers (ADMM for short) algorithm and several heuristic algorithms are introduced to provide approximate solutions for these two problems.
Date of Conference: 15-18 December 2015
Date Added to IEEE Xplore: 11 February 2016
ISBN Information:
Conference Location: Osaka, Japan

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