Abstract
Social networks are in continuous evolution, and its spreading has attracted the interest of both practitioners and the scientific community. In the last decades, several new interesting problems have aroused in the context of social networks, mainly due to an overabundance of information, usually named as infodemic. This problem emerges in several areas, such as viral marketing, disease prediction and prevention, and misinformation, among others. Then, it is interesting to identify the most influential users in a network to analyze the information transmitted, resulting in Social Influence Maximization (SIM) problems. In this research, the Budget Influence Maximization Problem (BIMP) is tackled. BIMP proposes a realistic scenario where the cost of selecting each node is different. This is modeled by having a budget that can be spent to select the users of a network, where each user has an associated cost. Since BIMP is a hard optimization problem, a metaheuristic algorithm based on Greedy Randomized Adaptive Search (GRASP) framework is proposed.
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.
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.