Abstract
The study of Social Network Influence has attracted the interest of scientists. The wide variety of real-world applications of this area, such as viral marketing and disease analysis, highlights the relevance of designing an algorithm capable of solving the problem efficiently. This paper studies the Multiple Round Influence Maximization (MRIM) problem, in which influence is propagated in multiple rounds independently from possibly different seed sets. This problem has two variants: the non-adaptive MRIM, in which the advertiser needs to determine the seed sets for all rounds at the beginning, and the adaptive MRIM, in which the advertiser can select the seed sets adaptively based on the propagation results in the previous rounds. The main difficulty of this optimization problem lies in the computational effort required to evaluate a solution. Since each node is infected with a certain probability, the value of the objective function must be calculated through an influence diffusion model, which results in a computationally complex process. For this purpose, a metaheuristic algorithm based on Variable Neighborhood Search is proposed with the aim of providing high-quality solutions, being competitive with the state of the art.
Isaac Lozano-Osorio
Artificial Intelligence Phd Student
Isaac Lozano graduated with a double degree in Computer Engineering and Computer Engineering from the Universidad Rey Juan Carlos, where he was awarded the prize for the Best Final Project. Subsequently, he completed a Master in Artificial Intelligence Research (UIMP). His main research interests are focused on the interface between Computer Science, Artificial Intelligence and Operations Research. Most of his publications deal with the development of metaheuristic procedures for graph modeled optimization problems.
Jesús Sánchez-Oro
Associate Professor
Associate Professor at the Computer Science Department, being one of the senior researchers of the Group for Research on Algorithms For Optimization GRAFO.
Abraham Duarte
Full Professor
Abraham Duarte is Full Professor in the Computer Science Department at the Rey Juan Carlos University (Madrid, Spain). He has done extensive research in the interface between computer science, artificial intelligence, and operations research to develop solution methods based on Computational Intelligence (metaheuristics) for practical problems in operations-management areas such as logistics and supply chains, telecommunications, decision-making under uncertainty and optimization of simulated systems.