What Characteristics Define a Good Solution in Social Influence Minimization Problems?

Abstract

The evolution of Social Networks has introduced significant challenges related to information overload. These challenges are covered in diverse areas, such as viral marketing or misinformation control. As social networks grow in complexity, the essential need to leverage data-driven insights becomes evident. The aim of the Social Influence Minimization Problem (IMP) is to identify and strategically block users to curtail information dissemination. Structural insights can be extracted through data-mining techniques to guide the design of efficient heuristics and the identification of influential users to be blocked. Considering good and bad quality solutions, a supervised learning approach is used to classify the extracted features, that allowed meaningful conclusions to be drawn regarding the features of these solutions. The IMP is addressed through the proposal of a robust heuristic method, based on the most relevant features, which is effective and efficient when compared with the state-of-the-art approaches.

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.