Examinando por Autor "Sanchez-Oro, Jesus"

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On the analysis of the influence of the evaluation metric in community detection over social networks
(MDPI, 2019) Perez-Pelo, Sergio; Sanchez-Oro, Jesus; Martin-Santamaria, Raul; Duarte, Abraham
Community detection in social networks is becoming one of the key tasks in social network analysis, since it helps with analyzing groups of users with similar interests. As a consequence, it is possible to detect radicalism or even reduce the size of the data to be analyzed, among other applications. This paper presents a metaheuristic approach based on Greedy Randomized Adaptive Search Procedure (GRASP) methodology for detecting communities in social networks. The community detection problem is modeled as an optimization problem, where the objective function to be optimized is the modularity of the network, a well-known metric in this scientific field. The results obtained outperform classical methods of community detection over a set of real-life instances with respect to the quality of the communities detected.
Solving the regenerator location problem with an iterated greedy approach
(Elsevier, 2021) Quintana, Juan David; Martin-Santamaria, Raúl; Sanchez-Oro, Jesus; Duarte, Abraham
The evolution of digital communications has resulted in new services that require from secure and robust connections. Nowadays, a signal must be transmitted to distant nodes, and the quality of the signal deteriorates as the distance between the endpoints increases. Regenerators are special components that are able to restore the signal, in order to increase the distance that the signal can travel without losing quality. These special components are very expensive to deploy and maintain and, for this reason, it is desirable to deploy the minimum number of regenerators in a network. We propose a metaheuristic algorithm based on the Iterated Greedy methodology to tackle the Regenerator Location Problem, whose objective is to minimize the number of regenerators required in a network. The extensive computational experiments show the performance of the proposed method compared with the best previous algorithm found in the state of the art.
Strategic oscillation for the balanced minimum sum-of-squares clustering problem
(Elsevier, 2021) Martin-Santamaria, Raul; Sanchez-Oro, Jesus; Perez-Pelo, Sergio; Duarte, Abraham
In the age of connectivity, every person is constantly producing large amounts of data every minute: social networks, information about trips, work connections, etc. These data will only become useful information if we are able to analyze and extract the most relevant features from it, which depends on the field of analysis. This task is usually performed by clustering data into similar groups with the aim of finding similarities and differences among them. However, the vast amount of data available makes traditional analysis obsolete for real-life datasets. This paper addresses the problem of dividing a set of elements into a predefined number of equally-sized clusters. In order to do so, we propose a Strategic Oscillation approach combined with a Greedy Randomized Adaptive Search Procedure. The computational experiments section firstly tunes the parameters of the algorithm and studies the influence of the proposed strategies. Then, the best variant is compared with the current state-of-the-art method over the same set of instances. The obtained results show the superiority of the proposal using two different clustering metrics: MSE (Mean Square Error) and Davies-Bouldin index.