Examinando por Autor "Romance, M."

Mostrando 1 - 6 de 6

Complex Networks applied to Dynamic Vision Systems
(Yamir Moreno. Universidad Rey Juan Carlos/ Regino Criado. Universidad Rey Juan Carlos, 2011-10-24) Criado, R.; Romance, M.; Sanchez, A.; Suarez, P.D.
We presented an novel application of complex network theory to the visual tracking problem in videos considering multiple targets. The implemented system has been tested successfully with different videos of different spatial resolu- tions producing accurate targets' detection and tracking at a real-time framerate.
Interest Point Detection in Images by a Local Centrality Algorithm on Complex Networks
(Yamir Moreno. Universidad Rey Juan Carlos/ Regino Criado. Universidad Rey Juan Carlos, 2011-04-01) Criado, R.; Romance, M.; Sanchez, A.
The field of Digital Image Processing offers an interesting framework for the application of the Complex Networks theory. Since images can be seen as organized data structures of adjacent pixels, it becomes natural to model and analyze them using complex network properties. We present a local centrality algorithm on a network with the aim detect the position and importance of interest points (i.e. corners) in a digital image. An spatial and weighted complex network is associated to the image and a new method for locating these feature points based on a local centrality measure of the corresponding network, is proposed.
Line graphs for directed and undirected networks: An structural and analytical comparison
(Yamir Moreno. Universidad Rey Juan Carlos/ Regino Criado. Universidad Rey Juan Carlos, 2011-10-24) Criado, R.; Flores, J.; Garcia del Amo, A.; Romance, M.
The centrality and e±ciency measures of a network G are strongly related to the respective measures on the associated line graph L(G) and bipartite graph B(G) as was shown in [8]. In this note we consider different ways to obtain a line graph from a given directed or undirected network and we obtain some interesting relations.
Matrix growth models based on centrality measures: a first analysis
(Yamir Moreno. Universidad Rey Juan Carlos/ Regino Criado. Universidad Rey Juan Carlos, 2011-10-24) Pedroche, F.; Criado, R.; Garcia, E.; Romance, M.
A general growth model of random networks based on centrality measures is introduced. This formalism extends the well-known models of prefer- ential attachment. We propose to set the preferential attachment using a linear function of some centrality measures ranging from local to global scale. The aim is to include spectral measures, such as PageRank and Bonacich, and geodesic measures, such as betweenness and closeness. In this paper we present a first analysis using degree and Personalized PageRank.
Some approaches to complex networks vulnerability: results and relationships
(2008-12-17) Criado, R.; Romance, M.
The structure and dynamics of networks with higher order interactions
(Elsevier, 2023) Boccaletti, S.; Lellis, P. De; Genio, C.I. del; Alfaro-Bittner, K.; Criado, R.; Jalan, S.; Romance, M.
All beauty, richness and harmony in the emergent dynamics of a complex system largely depend on the specific way in which its elementary components interact. The last twenty-five years have seen the birth and development of the multidisciplinary field of Network Science, wherein a variety of distributed systems in physics, biology, social sciences and engineering have been modeled as networks of coupled units, in the attempt to unveil the mechanisms underneath their observed functionality. There is, however, a fundamental limit to such a representation: networks capture only pairwise interactions, whereas the functioning of many real-world systems not only involves dyadic connections, but rather is the outcome of collective actions at the level of groups of nodes. For instance, in ecological systems, three or more species may compete for food or territory, and similar multi-component interactions appear in functional and structural brain networks, protein interaction networks, semantic networks, multi-authors scientific collaborations, offline and online social networks, gene regulatory networks and spreading of consensus or contagious diseases due to multiple, simultaneous, contacts. Such multi-component interactions can only be grasped through either hypergraphs or simplicial complexes, which indeed have recently found a huge number of applications. In this report, we cover the extensive literature of the past years on this subject, and we focus on the structure and dynamics of hypergraphs and simplicial complexes. These are indeed becoming increasingly relevant, thanks to the enhanced resolution of data sets and the recent advances in data analysis techniques, which (concurrently and definitely) have shown that such structures play a pivotal role in the complex organization and functioning of real-world distributed systems.