The evolution and spread of social networks have attracted the interest of the scientific community in the last few years. Specifically, several new interesting problems, which are hard to solve, have arisen in the context of viral marketing, disease analysis, and influence analysis, among others. Companies and researchers try to find the elements that maximize profit, stop pandemics, etc. This family of problems is collected under the term Social Network Influence Maximization problem (SNIMP), whose goal is to find the most influential users (commonly known as seeds) in a social network, simulating an influence diffusion model. SNIMP is known to be an $$\mathcal {NP}$$NP -hard problem and, therefore, an exact algorithm is not suitable for solving it optimally in reasonable computing time. The main drawback of this optimization problem lies on the computational effort required to evaluate a solution. Since each node is infected with a certain probability, the objective function value must be calculated through a Monte Carlo simulation, resulting in a computationally complex process. The current proposal tries to overcome this limitation by considering a metaheuristic algorithm based on the Greedy Randomized Adaptive Search Procedure (GRASP) framework to design a quick solution procedure for the SNIMP. Our method consists of two distinct stages: construction and local search. The former is based on static features of the network, which notably increases its efficiency since it does not require to perform any simulation during construction. The latter involves a local search based on an intelligent neighborhood exploration strategy to find the most influential users based on swap moves, also aiming for an efficient processing. Experiments performed on 7 well-known social network datasets with 5 different seed set sizes confirm that the proposed algorithm is able to provide competitive results in terms of quality and computing time when comparing it with the best algorithms found in the state of the art.

Teaching programming is a topic that has generated a high level of interest among researchers in recent decades. In particular, multiple approaches to teaching visual programming have been explored, from the use of tools such as Scratch, robots, unplugged programming or activities for the development of computational thinking. Despite the wide range of resources used, students generally tend to perform poorly academically and perceive learning visual programming as a complex and demotivating task. In this article, the TutoApp system is proposed together with a new methodology based on “Learning by Teaching”, where students create tutorials in their mobile devices to explain programming concepts to their peers. The hypothesis of this paper is that the use of this tool improves learning outcomes and the level of student satisfaction. An experiment with a pre-post-test design has been carried out with 57 university students in an introductory programming course, 30 belonging to a control group (did not use TutoApp) and 27 belonging to the experimental group (used TutoApp). The findings indicate that the creation of tutorials with TutoApp significantly improved students' academic performance over those who did not use it, specifically in learning the loops and conditional control structures. However, it was observed that anxiety increased in all students while learning visual programming. The results of this study open the door to the validation of the use of systems and methodologies for creating tutorials for teaching visual programming to university students.

This paper deals with an interesting facility location problem known as the bi-objective p-Median and p-Dispersion problem (BpMD problem). The BpMD problem seeks to locate p facilities to service a set of n demand points, and the goal is to minimize the total distance between facilities and demand points and, simultaneously, maximize the minimum distance between all pairs of hosted facilities. The problem is addressed with a novel path relinking approach, called reactive path relinking, which hybridizes two of the most extended path relinking variants: interior path relinking and exterior path relinking. Additionally, the proposal is adapted to a multi-objective perspective for finding a good approximation of the Pareto front. Computational results prove the superiority of the proposed algorithm over the best procedures found in the literature.