Abstract
This research proposes the use of metaheuristics for solving the Target Set Selection (TSS) problem. This problem emerges in the context of influence maximization problems, in which the objective is to maximize the number of active users when spreading information throughout a social network. Among all the influence maximization variants, TSS introduces the concept of reward of each user, which is the benefit associated to its activation. Therefore, the problem tries to maximize the reward obtained among all active users by selecting an initial set of users. Each user has also associated an activation cost, and the total sum of activation costs of the initial set of selected users cannot exceed a certain budget. In particular, two Path Relinking approaches are proposed, comparing them with the best method found in the state of the art. Additionally, a more challenging set of instances are derived from real-life social networks, where the best previous method is not able to find a feasible solution. The experimental results show the efficiency and efficacy of the proposal, supported by non-parametric statistical tests.
Isaac Lozano-Osorio
Phd in Artificial Intelligence
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.
Andrea Oliva-García
Collaborator
My research focuses on the use of metaheuristics and the social network problems solving.
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.