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Listar por autor "Pedroche Sánchez, Francisco"

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A biplex approach to PageRank centrality: from classic to multiplex networks

Pedroche Sánchez, Francisco; Romance, Miguel; Criado Herrero, Regino (American Institute of Physics, 2016-06-02)

In this paper, we present a new view of the PageRank algorithm inspired by multiplex networks. This new approach allows to introduce a new centrality measure for classic complex networks and a new proposal to extend the ...
A model to classify users of social networks based on PageRank

Pedroche Sánchez, Francisco (World Scientific Publishing, 2012-07)

In this paper, we present a model to classify users of Social Networks. In particular, we focus on Social Network Sites. The model is based on the PageRank algorithm. We use the personalization vector to bias the PageRank ...
A new method for comparing rankings through complex networks: Model and analysis of competitiveness of major European soccer leagues

Criado Herrero, Regino; García González, Esther; Pedroche Sánchez, Francisco; Romance, Miguel (American Institute of Physics (AIP), 2013-10-31)

In this paper, we show a new technique to analyze families of rankings. In particular, we focus on sports rankings and, more precisely, on soccer leagues. We consider that two teams compete when they change their relative ...
An Eigenvector Centrality for Multiplex Networks with Data

Pedroche Sánchez, Francisco; Tortosa, Leandro; Vicent Francés, J.F. (MDPI AG, 2019-06)

[EN] Networks are useful to describe the structure of many complex systems. Often, understanding these systems implies the analysis of multiple interconnected networks simultaneously, since the system may be modelled by ...
Combining the Two-Layers PageRank Approach with the APA Centrality in Networks with Data

Agryzkov, Taras; Pedroche Sánchez, Francisco; Tortosa, Leandro; Vicent, José F. (MDPI AG, 2018-12)

[EN] Identifying the influential nodes in complex networks is a fundamental and practical topic at the moment. In this paper, a new centrality measure for complex networks is proposed based on two contrasting models that ...
Comparing series of rankings with ties by using complex networks: An analysis of the Spanish stock market (IBEX-35 index)

Pedroche Sánchez, Francisco; Criado, Regino; García, Esther Higueras; Romance, Miguel; Sánchez, Victoria E. (American Institute of Mathematical Sciences (AIMS), 2015-03)

In this paper we extend the concept of Competitivity Graph to compare series of rankings with ties (partial rankings). We extend the usual method used to compute Kendall's coe cient for two partial rankings to the ...
Competitivity graphs analysis and structural comparison of rankings

Criado, Regino; García, Esther; Pedroche Sánchez, Francisco; Romance, Miguel; Sánchez-García, Victoria E. (Interlude, 2013-12-31)

[EN] A complex networks based method is introduced for comparing different complete rankings of a finite family of elements. The concepts of competitivity graph and evolutive competitivity graph are introduced as the main ...
Consensus over Multiplex Network To Calculate User Influence in Social Networks

Rebollo Pedruelo, Miguel; Del Val Noguera, Elena; Carrascosa Casamayor, Carlos; Palomares Chust, Alberto; Pedroche Sánchez, Francisco (Interlude, 2013)

[EN] User influence determines how the information is transmitted. Most of the current methods need to consider the complete network and, if it changes, the calculations have to be repeated from the scratch. This work ...
Convergence of Weighted-average consensus for undirected graphs

Pedroche Sánchez, Francisco; Rebollo Pedruelo, Miguel; Carrascosa Casamayor, Carlos; Palomares Chust, Alberto (Interlude, 2014-03-01)

[EN] In this note we address the problem of reaching a consensus in an undirected network where the nodes interchange information with their neighbors. Each node is provided with a value X_i and a weight w_i. The specific ...
Corrected Evolutive Kendall's tau Coefficients for Incomplete Rankings with Ties: Application to Case of Spotify Lists

Pedroche Sánchez, Francisco; Conejero, J. Alberto (MDPI AG, 2020-10)

[EN] Mathematical analysis of rankings is essential for a wide range of scientific, public, and industrial applications (e.g., group decision-making, organizational methods, R&D sponsorship, recommender systems, voter ...
Extending the Adapted PageRank Algorithm Centrality to Multiplex Networks with Data Using the PageRank Two-Layer Approach

Agryzkov, Taras; Curado, Manuel; Pedroche Sánchez, Francisco; Tortosa, Leandro; Vicent, Jose F. (MDPI AG, 2019-02)

[EN] Usually, the nodes' interactions in many complex networks need a more accurate mapping than simple links. For instance, in social networks, it may be possible to consider different relationships between people. This ...
Leadership groups on Social Network Sites based on Personalized PageRank

Pedroche Sánchez, Francisco; Moreno, Francisco; González, Andrés; Valencia, Andrés (Elsevier, 2013-04)

n this paper we present a new framework to identify leaders in a Social Network Site using the Personalized PageRank vector. The methodology is based on the concept of Leadership group recently introduced by one of the ...
Modelling Social Network Sites with PageRank and Social Competences

Pedroche Sánchez, Francisco (Interlude, 2011-04-01)

[EN] In this communication a recent method to classify the users of an SNS into Competitivity groups is recalled. This method is based on the PageRank algorithm. Competitivity groups are sets of nodes that compete among ...
On graphs associated to sets of rankings

Criado Herrero, Regino; García, Esther; Pedroche Sánchez, Francisco; Romance, Miguel (Elsevier, 2016-01-01)

In this paper we analyze families of rankings by studying structural properties of graphs. Given a finite number of elements and a set of rankings of those elements, two elements compete when they exchange their relative ...
On PageRank versatility for multiplex networks: properties and some useful bounds

Pedroche Sánchez, Francisco; Criado, Regino; Flores, Julio; García, Esther; Romance, Miguel (John Wiley & Sons, 2020-09-30)

[EN] In this paper, some results concerning the PageRank versatility measure for multiplex networks are given. This measure extends to the multiplex setting the well-known classic PageRank. Particularly, we focus on some ...
On some properties of the Laplacian matrix revealed by the RCM algorithm

Pedroche Sánchez, Francisco; Rebollo Pedruelo, Miguel; Carrascosa Casamayor, Carlos; Palomares Chust, Alberto (Akademie věd České republiky, Matematický ústav, 2016-10-13)

In this paper we present some theoretical results about the irreducibility of the Laplacian matrix ordered by the Reverse Cuthill-McKee (RCM) algorithm. We consider undirected graphs with no loops consisting of some connected ...
On the localization of the personalized PageRank of complex networks

García, E; Pedroche Sánchez, Francisco; Romance, M (Elsevier, 2012-12)

In this paper new results on personalized PageRank are shown. We consider directed graphs that may contain dangling nodes. The main result presented gives an analytical characterization of all the possible values of the ...
On the spectrum of two-layer approach and Multiplex PageRank

Pedroche Sánchez, Francisco; García, Esther; Romance, Miguel; Criado Herrero, Regino (Elsevier, 2018-12-15)

[EN] In this paper, we present some results about the spectrum of the matrix associated with the computation of the Multiplex PageRank defined by the authors in a previous paper. These results can be considered as a natural ...
Parametric controllability of the personalized PageRank: Classic model vs biplex approach

Flores, Julio; García, Esther; Pedroche Sánchez, Francisco; Romance, Miguel (American Institute of Physics, 2020-02)

[EN] Measures of centrality in networks defined by means of matrix algebra, like PageRank-type centralities, have been used for over 70 years. Recently, new extensions of PageRank have been formulated and may include a ...
Sharp estimates for the personalized Multiplex PageRank

Pedroche Sánchez, Francisco; García, Esther; Romance, Miguel; Criado Herrero, Regino (Elsevier, 2018-03-01)

[EN] PageRank can be understood as the stationary distribution of a Markov chain that occurs in a two-layer network with the same set of nodes in both layers: the physical layer and the teleportation layer. In this paper ...