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Sensitivity of complex networks measurements

BOAS, P. R. Villas; RODRIGUES, F. A.; TRAVIESO, Gonzalo; COSTA, Luciano da Fontoura
Fonte: IOP PUBLISHING LTD Publicador: IOP PUBLISHING LTD
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
45.98%
Complex networks obtained from real-world networks are often characterized by incompleteness and noise, consequences of imperfect sampling as well as artifacts in the acquisition process. Because the characterization, analysis and modeling of complex systems underlain by complex networks are critically affected by the quality and completeness of the respective initial structures, it becomes imperative to devise methodologies for identifying and quantifying the effects of the sampling on the network structure. One way to evaluate these effects is through an analysis of the sensitivity of complex network measurements to perturbations in the topology of the network. In this paper, measurement sensibility is quantified in terms of the relative entropy of the respective distributions. Three particularly important kinds of progressive perturbations to the network are considered, namely, edge suppression, addition and rewiring. The measurements allowing the best balance of stability (smaller sensitivity to perturbations) and discriminability (separation between different network topologies) are identified with respect to each type of perturbation. Such an analysis includes eight different measurements applied on six different complex networks models and three real-world networks. This approach allows one to choose the appropriate measurements in order to obtain accurate results for networks where sampling bias cannot be avoided-a very frequent situation in research on complex networks.; FAPESP[05/00587-5]; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); FAPESP[07/506339]; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); FAPESP[08/53721-9]; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); CNPq[301303/06-1]

Shape classification using complex network and Multi-scale Fractal Dimension

BACKES, Andre Ricardo; BRUNO, Odemir Martinez
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
65.83%
Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V.; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); CNPq (National Council for Scientific and Technological Development, Brazil)[306628/2007-4]; CNPq (National Council for Scientific and Technological Development, Brazil)[484474/2007-3]; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); FAPESP (The State of Sao Paulo Research Foundation)[2006/54367-9]

A complex network-based approach for boundary shape analysis

BACKES, Andre Ricardo; CASANOVA, Dalcimar; BRUNO, Odemir Martinez
Fonte: PERGAMON-ELSEVIER SCIENCE LTD Publicador: PERGAMON-ELSEVIER SCIENCE LTD
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
65.87%
This paper introduces a novel methodology to shape boundary characterization, where a shape is modeled into a small-world complex network. It uses degree and joint degree measurements in a dynamic evolution network to compose a set of shape descriptors. The proposed shape characterization method has all efficient power of shape characterization, it is robust, noise tolerant, scale invariant and rotation invariant. A leaf plant classification experiment is presented on three image databases in order to evaluate the method and compare it with other descriptors in the literature (Fourier descriptors, Curvature, Zernike moments and multiscale fractal dimension). (C) 2008 Elsevier Ltd. All rights reserved.; CNPq; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); National Council for Scientific and Technological Development, Brazil; National Council for Scientific and Technological Development, Brazil[306628/2007-4]; FAPESP State of Sao Paulo Research Foundation[2006154367-9]; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); FAPESP State of Sao Paulo Research Foundation[2006/53972-6]; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); FAPESP[2006154367-9]; FAPESP[2006/53972-6]; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network

COSTA, Luciano da Fontoura; TOGNETTI, Marilza Aparecida Rodrigues; SILVA, Filipi Nascimento
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
55.94%
Differently from theoretical scale-free networks, most real networks present multi-scale behavior, with nodes structured in different types of functional groups and communities. While the majority of approaches for classification of nodes in a complex network has relied on local measurements of the topology/connectivity around each node, valuable information about node functionality can be obtained by concentric (or hierarchical) measurements. This paper extends previous methodologies based on concentric measurements, by studying the possibility of using agglomerative clustering methods, in order to obtain a set of functional groups of nodes, considering particular institutional collaboration network nodes, including various known communities (departments of the University of Sao Paulo). Among the interesting obtained findings, we emphasize the scale-free nature of the network obtained, as well as identification of different patterns of authorship emerging from different areas (e.g. human and exact sciences). Another interesting result concerns the relatively uniform distribution of hubs along concentric levels, contrariwise to the non-uniform pattern found in theoretical scale-free networks such as the BA model. (C) 2008 Elsevier B.V. All rights reserved.; FAPESP[05/00587-5]; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); CNPq[301303/06-1]; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); CNPq[133256/2007-3]; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Texture analysis and classification: a complex network-based approach

Backes, André Ricardo; Casanova, Dalcimar; Bruno, Odemir Martinez
Fonte: Elsevier; Philadelphia Publicador: Elsevier; Philadelphia
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
65.9%
In this paper, we propose a novel texture analysis method using the complex network theory. We investigated how a texture image can be effectively represented, characterized and analyzed in terms of a complex network. The proposed approach uses degree measurements to compose a set of texture descriptors. The results show that the method is very robust, and it presents a excellent texture discrimination for all considered classes, overcoming traditional texture methods.; CNPq (308449/2010-0, 473893/2010-0); FAPESP (11/01523-1, 06/54367-9, 08/57313-2)

Network-based data classification: combining k-associated optimal graphs and high-level prediction

Carneiro, Murillo G.; Rosa, João Luis Garcia; Lopes, Alneu de Andrade; Liang, Zhao
Fonte: Springer; Dordrecht Publicador: Springer; Dordrecht
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
55.95%
Background: Traditional data classification techniques usually divide the data space into sub-spaces, each representing a class. Such a division is carried out considering only physical attributes of the training data (e.g., distance, similarity, or distribution). This approach is called low-level classification. On the other hand, network or graph-based approach is able to capture spacial, functional, and topological relations among data, providing a so-called high-level classification. Usually, network-based algorithms consist of two steps: network construction and classification. Despite that complex network measures are employed in the classification to capture patterns of the input data, the network formation step is critical and is not well explored. Some of them, such as K-nearest neighbors algorithm (KNN) and -radius, consider strict local information of the data and, moreover, depend on some parameters, which are not easy to be set. Methods: We propose a network-based classification technique, named high-level classification on K-associated optimal graph (HL-KAOG), combining the K-associated optimal graph and high-level prediction. In this way, the network construction algorithm is non-parametric, and it considers both local and global information of the training data. In addition...

Clusterização de dados utilizando técnicas de redes complexas e computação bioinspirada; Data clustering based on complex network community detection

Oliveira, Tatyana Bitencourt Soares de
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 25/02/2008 PT
Relevância na Pesquisa
55.87%
A Clusterização de dados em grupos oferece uma maneira de entender e extrair informações relevantes de grandes conjuntos de dados. A abordagem em relação a aspectos como a representação dos dados e medida de similaridade entre clusters, e a necessidade de ajuste de parâmetros iniciais são as principais diferenças entre os algoritmos de clusterização, influenciando na qualidade da divisão dos clusters. O uso cada vez mais comum de grandes conjuntos de dados aliado à possibilidade de melhoria das técnicas já existentes tornam a clusterização de dados uma área de pesquisa que permite inovações em diferentes campos. Nesse trabalho é feita uma revisão dos métodos de clusterização já existentes, e é descrito um novo método de clusterização de dados baseado na identificação de comunidades em redes complexas e modelos computacionais inspirados biologicamente. A técnica de clusterização proposta é composta por duas etapas: formação da rede usando os dados de entrada; e particionamento dessa rede para obtenção dos clusters. Nessa última etapa, a técnica de otimização por nuvens de partículas é utilizada a fim de identificar os clusters na rede, resultando em um algoritmo de clusterização hierárquico divisivo. Resultados experimentais revelaram como características do método proposto a capacidade de detecção de clusters de formas arbitrárias e a representação de clusters com diferentes níveis de refinamento.; DAta clustering is an important technique to understand and to extract relevant information in large datasets. Data representation and similarity measure adopted...

Redes complexas: novas metodologias e modelagem de aquisição de conhecimento; Complex Networks: New methodologies and knowledge acquisition modeling

Silva, Filipi Nascimento
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 17/12/2009 PT
Relevância na Pesquisa
46.01%
Estudos em redes complexas têm ganhado cada vez mais atenção devido ao seu potencial de representação simples de modelos complexos em diversas áreas de conhecimento. A obtenção de modelos quantitativos que representem fenômenos observados da natureza, assim como o desenvolvimento de metodologias de caracterização de redes complexas, tornaram-se essenciais para a compreensão e desenvolvimento de pesquisas com essas estruturas. Este trabalho tem como objetivo desenvolver e estudar alguns métodos recentes, usados para a caracterização de redes complexas, explorando-os no contexto da modelagem de conhecimento. Para isso, duas redes complexas foram geradas, uma rede de colaboração de pesquisadores da USP e outra obtida a partir do banco de dados de artigos da Wikipédia, considerando apenas aqueles da categoria de teoremas matemáticos. As medidas concêntricas, que foram recentemente formalizadas, são exploradas e aplicadas às redes descritas, assim como para diversos modelos teóricos, fornecendo informações muito relevantes sobre a topologia dessas redes. Resultados ainda mais interessantes são obtidos pela caracterização dos vértices da rede de colaboração, que revelam padrões de interdisciplinaridade entre as diferentes áreas do conhecimento. Um modelo de aquisição de conhecimento também foi proposto...

Emprego de redes complexas no estudo das relações entre morfologia individual, topologia global e aspectos dinâmicos em neurociência; Employment of complex network theory on the study of the relations between individual morphology, global topology and dynamical aspects in Neuroscience

Silva, Renato Aparecido Pimentel da
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 03/05/2012 PT
Relevância na Pesquisa
55.86%
A teoria de redes complexas se consolidou nos últimos anos, graças ao seu potencial como ferramenta versátil no estudo de diversos sistemas discretos. É possível enumerar aplicações em áreas tão distintas como engenharia, sociologia, computação, linguística e biologia. Tem merecido atenção, por exemplo, o estudo da organização estrutural do cérebro, tanto em nível microscópico (em nível de neurônios) como regional (regiões corticais). Acredita-se que tal organização visa otimizar a dinâmica, favorecendo processos como sincronização e processamento paralelo. Estrutura e funcionamento, portanto, estão relacionados. Tal relação é abordada pela teoria de redes complexas nos mais diversos sistemas, sendo possivelmente seu principal objeto de estudo. Neste trabalho exploramos as relações entre aspectos estruturais de redes neuronais e corticais e a atividade nas mesmas. Especificamente, estudamos como a interconectividade entre o córtex e o tálamo pode interferir em estados de ativação do último, considerando-se o sistema tálamo-cortical do gato bem como alguns modelos para geração de rede encontrados na literatura. Também abordamos a relação entre a morfologia individual de neurônios e a conectividade em redes neuronais...

Redes complexas em visão computacional com aplicações em bioinformática; Complex networks in computer vision, with applications in bioinformatics

Casanova, Dalcimar
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 01/07/2013 PT
Relevância na Pesquisa
55.88%
Redes complexas é uma área de estudo relativamente recente, que tem chamado a atenção da comunidade científica e vem sendo aplicada com êxito em diferentes áreas de atuação tais como redes de computadores, sociologia, medicina, física, matemática entre outras. Entretanto a literatura demonstra que poucos são os trabalhos que empregam redes complexas na extração de características de imagens para posterior analise ou classificação. Dada uma imagem é possível modela-la como uma rede, extrair características topológicas e, utilizando-se dessas medidas, construir o classificador desejado. Esse trabalho objetiva, portanto, investigar mais a fundo esse tipo de aplicação, analisando novas formas de modelar uma imagem como uma rede complexa e investigar diferentes características topológicas na caracterização de imagens. Como forma de analisar o potencial das técnicas desenvolvidas, selecionamos um grande desafio na área de visão computacional: identificação vegetal por meio de análise foliar. A identificação vegetal é uma importante tarefa em vários campos de pesquisa como biodiversidade, ecologia, botânica, farmacologia entre outros.; Complex networks is a relatively recent field of study, that has called the attention of the scientific community and has been successfully applied in different areas such as computer networking...

Análise de modelo de Hopfield com topologia de rede complexa; Investigation of the Hopfield model with complex network topology

Sousa, Fabiano Berardo de
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 13/11/2013 PT
Relevância na Pesquisa
55.96%
Redes neurais biológicas contêm bilhões de células (neurônios) agrupadas em regiões espacial e funcionalmente distintas. Elas também apresentam comportamentos complexos, tais como dinâmicas periódicas e caóticas. Na área da Inteligência Artificial, pesquisas mostram que Redes Neurais Caóticas, isto é, modelos de Redes Neurais Artificiais que operam com dinâmicas complexas, são mais eficientes do que modelos tradicionais no que diz respeito a evitar memórias espúrias. Inspirado pelo fato de que o córtex cerebral contém agrupamentos de células e motivado pela eficiência no uso de dinâmicas complexas, este projeto de pesquisa investiga o comportamento dinâmico de um modelo de Rede Neural Artificial Recorrente, como o de Hopfield, porém com a topologia sináptica reorganizada a ponto de originar agrupamentos de neurônios, tal como acontece em uma Rede Complexa quando esta apresenta uma estrutura de comunidades. O modelo de treinamento tradicional de Hopfield também é alterado para uma regra de aprendizado que posta os padrões em ciclos, gerando uma matriz de pesos assimétrica. Resultados indicam que o modelo proposto oscila entre comportamentos periódicos e caóticos, dependendo do grau de fragmentação das sinapses. Com baixo grau de fragmentação...

Complex networks and data mining: toward a new perspective for the understanding of complex systems

Zanin, Massimiliano
Fonte: Universidade Nova de Lisboa Publicador: Universidade Nova de Lisboa
Tipo: Tese de Doutorado
Publicado em /12/2014 ENG
Relevância na Pesquisa
45.98%
Complex systems, i.e. systems composed of a large set of elements interacting in a non-linear way, are constantly found all around us. In the last decades, different approaches have been proposed toward their understanding, one of the most interesting being the Complex Network perspective. This legacy of the 18th century mathematical concepts proposed by Leonhard Euler is still current, and more and more relevant in real-world problems. In recent years, it has been demonstrated that network-based representations can yield relevant knowledge about complex systems. In spite of that, several problems have been detected, mainly related to the degree of subjectivity involved in the creation and evaluation of such network structures. In this Thesis, we propose addressing these problems by means of different data mining techniques, thus obtaining a novel hybrid approximation intermingling complex networks and data mining. Results indicate that such techniques can be effectively used to i) enable the creation of novel network representations, ii) reduce the dimensionality of analyzed systems by pre-selecting the most important elements, iii) describe complex networks, and iv) assist in the analysis of different network topologies. The soundness of such approach is validated through different validation cases drawn from actual biomedical problems...

Discovering the Influences of Complex Network Effects on Recovering Large Scale Multiagent Systems

Xu, Yang; Liu, Pengfei; Li, Xiang; Ren, Wei
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
45.99%
Building efficient distributed coordination algorithms is critical for the large scale multiagent system design, and the communication network has been shown as a key factor to influence system performance even under the same coordination protocol. Although many distributed algorithm designs have been proved to be feasible to build their functions in the large scale multiagent systems as claimed, the performances may not be stable if the multiagent networks were organized with different complex network topologies. For example, if the network was recovered from the broken links or disfunction nodes, the network topology might have been shifted. Therefore, their influences on the overall multiagent system performance are unknown. In this paper, we have made an initial effort to find how a standard network recovery policy, MPLS algorithm, may change the network topology of the multiagent system in terms of network congestion. We have established that when the multiagent system is organized as different network topologies according to different complex network attributes, the network shifts in different ways. Those interesting discoveries are helpful to predict how complex network attributes influence on system performance and in turn are useful for new algorithm designs that make a good use of those attributes.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Edalatmanesh, Mahmood
Fonte: Brock University Publicador: Brock University
Tipo: Electronic Thesis or Dissertation
ENG
Relevância na Pesquisa
45.98%
Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network...

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

Harrison, Kyle Robert
Fonte: Brock University Publicador: Brock University
Tipo: Electronic Thesis or Dissertation
ENG
Relevância na Pesquisa
46.03%
A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves...

Improved Biomolecular Crystallography at Low Resolution with the Deformable Complex Network Approach

Zhang, Chong
Fonte: Universidade Rice Publicador: Universidade Rice
ENG
Relevância na Pesquisa
65.83%
It is often a challenge to atomically determine the structure of large macromolecular assemblies, even if successfully crystallized, due to their weak diffraction of X-rays. Refinement algorithms that work with low-resolution diffraction data are necessary for researchers to obtain a picture of the structure from limited experimental information. Relationship between the structure and function of proteins implies that a refinement approach delivering accurate structures could considerably facilitate further research on their function and other related applications such as drug design. Here a refinement algorithm called the Deformable Complex Network is presented. Computation results revealed that, significant improvement was observed over the conventional refinement and DEN refinement, across a wide range of test systems from the Protein Data Bank, indicated by multiple criteria, including the free R value, the Ramachandran Statistics, the GDT (<1Å) score, TM-score as well as associated electron density map.

On the outer synchronization of complex dynamical networks

Asheghan, Mohammad Mostafa
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Tese de Doutorado
ENG
Relevância na Pesquisa
46%
Complex network models have become a major tool in the modeling and analysis of many physical, biological and social phenomena. A complex network exhibits behaviors which emerge as a consequence of interactions between its constituent elements, that is, remarkably, not the same as individual components. One particular topic that has attracted the researchers' attention is the analysis of how synchronization occurs in this class of models, with the expectation of gaining new insights of the interactions taking place in real-world complex systems. Most of the work in the literature so far has been focused on the synchronization of a collection of interconnected nodes (forming one single network), where each node is a dynamical system governed by a set of nonlinear differential equations, possibly displaying chaotic dynamics. In this thesis, we study an extended version of this problem. In particular, we consider a setup consisting of two complex networks which are coupled unidirectionally, in such a way that a set of signals from the master network are injected into the response network, and then investigate how synchronization is attained. Our analysis is fairly general. We impose few conditions on the network structure and do not assume that the nodes in a single network are synchronized. This work can be divided into two main parts; outer synchronization in fractional-order networks...

A Novel BA Complex Network Model on Color Template Matching

Han, Risheng; Shen, Shigen; Yue, Guangxue; Ding, Hui
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
45.99%
A novel BA complex network model of color space is proposed based on two fundamental rules of BA scale-free network model: growth and preferential attachment. The scale-free characteristic of color space is discovered by analyzing evolving process of template's color distribution. And then the template's BA complex network model can be used to select important color pixels which have much larger effects than other color pixels in matching process. The proposed BA complex network model of color space can be easily integrated into many traditional template matching algorithms, such as SSD based matching and SAD based matching. Experiments show the performance of color template matching results can be improved based on the proposed algorithm. To the best of our knowledge, this is the first study about how to model the color space of images using a proper complex network model and apply the complex network model to template matching.

Deformable complex network for refining low-resolution X-ray structures

Zhang, Chong; Wang, Qinghua; Ma, Jianpeng
Fonte: Universidade Rice Publicador: Universidade Rice
Tipo: Journal article; Text; publisher version
ENG
Relevância na Pesquisa
65.89%
In macromolecular X-ray crystallography, building more accurate atomic models based on lower resolution experimental diffraction data remains a great challenge. Previous studies have used a deformable elastic network (DEN) model to aid in low-resolution structural refinement. In this study, the development of a new refinement algorithm called the deformable complex network (DCN) is reported that combines a novel angular network-based restraint with the DEN model in the target function. Testing of DCN on a wide range of low-resolution structures demonstrated that it constantly leads to significantly improved structural models as judged by multiple refinement criteria, thus representing a new effective refinement tool for low-resolution structural determination.

Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems

Marwan, Norbert; Kurths, Jürgen
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/07/2015
Relevância na Pesquisa
45.99%
We present here two promising techniques for the application of the complex network approach to continuous spatio-temporal systems that have been developed in the last decade and show large potential for future application and development of complex systems analysis. First, we discuss the transforming of a time series from such systems to a complex network. The natural approach is to calculate the recurrence matrix and interpret such as the adjacency matrix of an associated complex network, called recurrence network. Using complex network measures, such as transitivity coefficient, we demonstrate that this approach is very efficient for identifying qualitative transitions in observational data, e.g., when analyzing paleoclimate regime transitions. Second, we demonstrate the use of directed spatial networks constructed from spatio-temporal measurements of such systems that can be derived from the synchronized-in-time occurrence of extreme events in different spatial regions. Although there are many possibilities to investigate such spatial networks, we present here the new measure of network divergence and how it can be used to develop a prediction scheme of extreme rainfall events.; Comment: 10 pages, 8 figures