Relevant bibliographies by topics / Complex network

Academic literature on the topic 'Complex network'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Complex network"

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Hu, Ziping, Krishnaiyan Thulasiraman, and Pramode K. Verma. "Complex Networks: Traffic Dynamics, Network Performance, and Network Structure." American Journal of Operations Research 03, no. 01 (2013): 187–95. http://dx.doi.org/10.4236/ajor.2013.31a018.

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Tan, Yangxin, Junlin Wu, and Qing Zhong. "Complex network." Journal of Physics: Conference Series 1601 (July 2020): 032011. http://dx.doi.org/10.1088/1742-6596/1601/3/032011.

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Maciá-Pérez, Francisco, Iren Lorenzo-Fonseca, Jose Vicente Berná-Martinez, and Jose Manuel Sánchez-Bernabeu. "Conceptual Modelling of Complex Network Management Systems." Journal of Computers 10, no. 5 (2015): 309–20. http://dx.doi.org/10.17706/jcp.10.5.309-320.

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Xu, Shuai, and Bai Da Zhang. "Complex Network Model and its Application." Advanced Materials Research 791-793 (September 2013): 1589–92. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1589.

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Human life is in a complex network world. In everyday life, the network can be a physical object such as the Internet, power network, road network and neural network; can also abstract not touch, such as interpersonal networks, networks of co-operation in scientific research, product supply chain network, biological populations, networks, etc.. The topology of these networks, the statistical characteristics and the formation mechanism, and so on, has a very important significance for the efficient allocation of resources, provides various functions, as well as the stability of the network, however, due to the complexity of these networks, conventional simplified model and cannot be good solution to the above problems. The complex network and network complexity has become a hot issue in the scientific and engineering concern. This article describes a few common complex network models and its application brief.
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Asbaş, Caner, Zühal Şenyuva, and Şule Tuzlukaya. "New Organizations in Complex Networks: Survival and Success." Central European Management Journal 30, no. 1 (March 15, 2022): 11–39. http://dx.doi.org/10.7206/cemj.2658-0845.68.

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Purpose: The present study investigates the survival and success of new organizations in the light of complex network theory. Methodology: The empirical data was collected using the survey method from the technology park companies are analyzed with social network analysis. Two main methods were used in this study: descriptive statistics and social network analysis. Findings: The findings indicate that new nodes appearing because of splitting up of bigger nodes from present or other related networks have a higher degree of centrality. In practice, this means that companies founded by former members of large-scale companies from these networks are more successful due to the ease in providing the flow of resources and information through previous links. This suggests that the imprint effect can be observed in the appearance, lifecycle, and performance of new nodes in complex networks. Originality: The literature lacks studies on new organizations’ lifecycle in complex networks despite the existence of studies about new organizations in organizational networks. This study examines the appearance, success, and survival of new organizations in networks by complex network approaches such as dynamism, dissipative structures, and uncertainties.
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Abe, S., and N. Suzuki. "Complex-network description of seismicity." Nonlinear Processes in Geophysics 13, no. 2 (May 9, 2006): 145–50. http://dx.doi.org/10.5194/npg-13-145-2006.

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Abstract. The seismic data taken in California and Japan are mapped to growing random networks. It is shown in the undirected network picture that these earthquake networks are scale-free and small-work networks with the power-law connectivity distributions, the large values of the clustering coefficient, and the small values of the average path length. It is demonstrated how the present network approach reveals complexity of seismicity in a novel manner.
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Guo, Dong Wei, Xiang Yan Meng, and Cai Fang Hou. "Building Complex Network Similar to Facebook." Applied Mechanics and Materials 513-517 (February 2014): 909–13. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.909.

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Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.
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Tarapata, Zbigniew. "Modelling and analysis of transportation networks using complex networks: Poland case study." Archives of Transport 36, no. 4 (December 31, 2015): 55–65. http://dx.doi.org/10.5604/08669546.1185207.

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In the paper a theoretical bases and empirical results deal with analysis and modelling of transportation networks in Poland using complex networks have been presented. Properties of complex networks (Scale Free and Small World) and network's characteristic measures have been described. In this context, results of empirical researches connected with characteristics of passenger air links network, express railway links network (EuroCity and InterCity) and expressways/highways network in Poland have been given. For passenger air links network in Poland results are compared with the same networks in USA, China, India, Italy and Spain. In the conclusion some suggestions, observations and perspective dealing with complex network in transportation networks have been presented.
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PELLEGRINI, Lilla, Monica LEBA, and Alexandru IOVANOVICI. "CHARACTERIZATION OF URBAN TRANSPORTATION NETWORKS USING NETWORK MOTIFS." Acta Electrotechnica et Informatica 20, no. 4 (January 21, 2020): 3–9. http://dx.doi.org/10.15546/aeei-2020-0019.

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We use tools and techniques specific to the field of complex networks analysis for the identification and extraction of key parameters which define ”good” patterns and practices for designing public transportation networks. Using network motifs we analyze a set of 18 cities using public data sets regarding the topology of network and discuss each of the identified motifs using the concepts and tools of urban planning.
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Fang, Yimeng. "Robustness analysis of highway network based on complex network." Highlights in Science, Engineering and Technology 42 (April 7, 2023): 291–97. http://dx.doi.org/10.54097/hset.v42i.7108.

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Taking Chongqing, Hunan, Shandong, Shaanxi and Sichuan as examples, this paper conducts a comparative study on the robustness of their highway networks, which is helpful for the subsequent construction of China's highway networks. The topology structure of highway networks is studied by complex network theory. The degree distribution, average degree, average clustering coefficient, average path length, network diameter and other parameters of the network were calculated, and the robustness of the highway network in five provinces and cities was compared from four aspects: connectivity, network efficiency, turn rate and robustness r. The results show that Chongqing and Shaanxi have a good performance of highway network robustness, Shandong and Sichuan have a more balanced performance, and Hunan has a weak performance. Enhance network robustness by placing route planning directions in place with fewer route options to provide drivers with more route options.
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Dissertations / Theses on the topic "Complex network"

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Lordan, Oriol. "Airline route networks : a complex network approach." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/144526.

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Communication via air routes is an important issue in a world organized around a web-like city network. In this context, the robustness of network infrastructures, e.g. air transport networks, are a central issue in transport geography. Disruption of communication links by intentional causes (e.g., terrorist attack on an airport) or unintentional (e.g., weather inclemency) could be a serious drawback for countries, regions and airlines. Policymakers and the management of airlines and alliances should be able to reduce the effects of such interruptions in order to ensure good communication through air transport (i.e., maximize the robustness of their network at a reasonable cost). The literature review of the study of air transport route networks through an analysis of complex networks has highlighted a lack of contributions to the study of the topology and the robustness of such networks, which contrasts with advances undertaken for other transport networks or communication systems. The literatura survey suggests areas in which research should be undertaken, based on the existing literature in other areas and from three different perspectives: global route networks, airline alliances and airlines. The aim of this research is to develop a better understanding of air traffic and, in particular, to be able to assess the potential damage of any airport being inoperative for a continent, country or airline.
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Khorramzadeh, Yasamin. "Network Reliability: Theory, Estimation, and Applications." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/64383.

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Network reliability is the probabilistic measure that determines whether a network remains functional when its elements fail at random. Definition of functionality varies depending on the problem of interest, thus network reliability has much potential as a unifying framework to study a broad range of problems arising in complex network contexts. However, since its introduction in the 1950's, network reliability has remained more of an interesting theoretical construct than a practical tool. In large part, this is due to well-established complexity costs for both its evaluation and approximation, which has led to the classification of network reliability as a NP-Hard problem. In this dissertation we present an algorithm to estimate network reliability and then utilize it to evaluate the reliability of large networks under various descriptions of functionality. The primary goal of this dissertation is to pose network reliability as a general scheme that provides a practical and efficiently computable observable to distinguish different networks. Employing this concept, we are able to demonstrate how local structural changes can impose global consequences. We further use network reliability to assess the most critical network entities which ensure a network's reliability. We investigate each of these aspects of reliability by demonstrating some example applications.
Ph. D.
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Arulselvan, Ashwin. "Complex network assortment and modeling." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0014925.

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Jiang, Jian. "Modeling of complex network, application to road and cultural networks." Phd thesis, Université du Maine, 2011. http://tel.archives-ouvertes.fr/tel-00691129.

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Many complex systems arising from nature and human society can be described as complex networks. In this dissertation, on the basis of complex network theory, we pay attention to the topological structure of complex network and the dynamics on it. We established models to investigate the influences of the structure on the dynamics of networks and to shed light on some peculiar properties of complex systems. This dissertation includes four parts. In the first part, the empirical properties (degree distribution, clustering coefficient, diameter, and characteristic path length) of urban road network of Le Mans city in France are studied. The degree distribution shows a double power-law which we studied in detail. In the second part, we propose two models to investigate the possible mechanisms leading to the deviation from simple power law. In the first model, probabilistic addition of nodes and links, and rewiring of links are considered; in the second one, only random and preferential link growth is included. The simulation results of the modelling are compared with the real data. In the third part,the probabilistic uncertainty behavior of double power law distribution is investigated. The network optimization and optimal design of scale free network to random failures are discussed from the viewpoint of entropy maximization. We defined equilibrium network ensemble as stationary ensembles of graphs by using some thermodynamics like notions such as "energy", "temperature", "free energy" for network. In the forth part, an union-division model is established to investigate the time evolution of certain networks like cultural or economical networks. In this model, the nodes represent, for example, the cultures. Several quantities such as richness, age, identity, ingredient etc. are used to parameterize the probabilistic evolution of the network. The model offers a long term view on the apparently periodic dynamics of an ensemble of cultural or economic entities in interaction.
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Pimenta, Mayra Mercedes Zegarra. "Self-organization map in complex network." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-30102018-111955/.

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The Self-Organization Map (SOM) is an artificial neural network that was proposed as a tool for exploratory analysis in large dimensionality data sets, being used efficiently for data mining. One of the main topics of research in this area is related to data clustering applications. Several algorithms have been developed to perform clustering in data sets. However, the accuracy of these algorithms is data depending. This thesis is mainly dedicated to the investigation of the SOM from two different approaches: (i) data mining and (ii) complex networks. From the data mining point of view, we analyzed how the performance of the algorithm is related to the distribution of data properties. It was verified the accuracy of the algorithm based on the configuration of the parameters. Likewise, this thesis shows a comparative analysis between the SOM network and other clustering methods. The results revealed that in random configuration of parameters the SOM algorithm tends to improve its acuracy when the number of classes is small. It was also observed that when considering the default configurations of the adopted methods, the spectral approach usually outperformed the other clustering algorithms. Regarding the complex networks approach, we observed that the network structure has a fundamental influence of the algorithm accuracy. We evaluated the cases at short and middle learning time scales and three different datasets. Furthermore, we show how different topologies also affect the self-organization of the topographic map of SOM network. The self-organization of the network was studied through the partitioning of the map in groups or communities. It was used four topological measures to quantify the structure of the groups such as: modularity, number of elements per group, number of groups per map, size of the largest group in three network models. In small-world (SW) networks, the groups become denser as time increases. An opposite behavior is found in the assortative networks. Finally, we verified that if some perturbation is included in the system, like a rewiring in a SW network and the deactivation model, the system cannot be organized again. Our results enable a better understanding of SOM in terms of parameters and network structure.
Um Mapa Auto-organizativo (da sigla SOM, Self-organized map, em inglês) é uma rede neural artificial que foi proposta como uma ferramenta para análise exploratória em conjuntos de dados de grande dimensionalidade, sendo utilizada de forma eficiente na mineração de dados. Um dos principais tópicos de pesquisa nesta área está relacionado com as aplicações de agrupamento de dados. Vários algoritmos foram desenvolvidos para realizar agrupamento de dados, tendo cada um destes algoritmos uma acurácia específica para determinados tipos de dados. Esta tese tem por objetivo principal analisar a rede SOM a partir de duas abordagens diferentes: mineração de dados e redes complexas. Pela abordagem de mineração de dados, analisou-se como o desempenho do algoritmo está relacionado à distribuição ou características dos dados. Verificou-se a acurácia do algoritmo com base na configuração dos parâmetros. Da mesma forma, esta tese mostra uma análise comparativa entre a rede SOM e outros métodos de agrupamento. Os resultados revelaram que o uso de valores aleatórios nos parâmetros de configuração do algoritmo SOM tende a melhorar sua acurácia quando o número de classes é baixo. Observou-se também que, ao considerar as configurações padrão dos métodos adotados, a abordagem espectral usualmente superou os demais algoritmos de agrupamento. Pela abordagem de redes complexas, esta tese mostra que, se considerarmos outro tipo de topologia de rede, além do modelo regular geralmente utilizado, haverá um impacto na acurácia da rede. Esta tese mostra que o impacto na acurácia é geralmente observado em escalas de tempo de aprendizado curto e médio. Esse comportamento foi observado usando três conjuntos de dados diferentes. Além disso, esta tese mostra como diferentes topologias também afetam a auto-organização do mapa topográfico da rede SOM. A auto-organização da rede foi estudada por meio do particionamento do mapa em grupos ou comunidades. Foram utilizadas quatro medidas topológicas para quantificar a estrutura dos grupos em três modelos distintos de rede: modularidade, número de elementos por grupo, número de grupos por mapa, tamanho do maior grupo. Em redes de pequeno mundo, os grupos se tornam mais densos à medida que o tempo aumenta. Um comportamento oposto a isso é encontrado nas redes assortativas. Apesar da modularidade, tem um alto valor em ambos os casos.
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Kim, Hyoungshick. "Complex network analysis for secure and robust communications." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610134.

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Al-Musawi, Ahmad Jr. "COMPLEX NETWORK GROWING MODEL USING DOWNLINK MOTIFS." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3088.

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Understanding the underlying architecture of gene regulatory networks (GRNs) has been one of the major goals in systems biology and bioinformatics as it can provide insights in disease dynamics and drug development. Such GRNs are characterized by their scale-free degree distributions and existence of network motifs, which are small subgraphs of specific types and appear more abundantly in GRNs than in other randomized networks. In fact, such motifs are considered to be the building blocks of GRNs (and other complex networks) and they help achieve the underlying robustness demonstrated by most biological networks. The goal of this thesis is to design biological network (specifically, GRN) growing models. As the motif distribution in networks grown using preferential attachment based algorithms do not match that of the GRNs seen in model organisms like E. coli and yeast, we hypothesize that such models at a single node level may not properly reproduce the observed degree and motif distributions of biological networks. Hence, we propose a new network growing algorithm wherein the central idea is to grow the network one motif (specifically, we consider one downlink motif) at a time. The accuracy of our proposed algorithm was evaluated extensively and show much better performance than existing network growing models both in terms of degree and motif distributions. We also propose a complex network growing game that can identify important strategies behind motif interactions by exploiting human (i.e., gamer) intelligence. Our proposed gaming software can also help in educational purposes specifically designed for complex network studies.
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Silva, Diamantino Castanheira da. "Complex network view of envolving manifolds." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/21652.

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Mestrado em Física
Neste estudo investigamos redes complexas formadas por triangulações de variedades topológicas em evolução, localmente homeomórficas a um plano. O conjunto de transformações dessas redes é restringida pela condição de que a cada passo todas as faces se mantenham triangulares. Neste trabalho adotamos duas abordagens principais. Na primeira abordagem crescemos variedades usando várias regras simples, que progressivamente adicionam novos triângulos. Na outra abordagem relaxamos a estrutura de variedades grandes, mantendo o número de triângulos constante. As redes resultantes da evolução destas triangulações demonstram várias características interessantes e inesperadas em redes planares, tais como diâmetros ”small-world” e distribuições de grau tipo lei de potência. Finalmente manipulámos a topologia das variedades pela introdução de ”wormholes”. A presença de ”wormholes” pode mudar a estrutura da rede significamente, dependendo da taxa a que são introduzidos. Se introduzirmos ”wormholes” a uma taxa constante, o diâmetro da rede apresenta um crescimento sub-logarítmico com o número de nodos do sistema.
We study complex networks formed by triangulations of evolving manifolds, locally homeomorphic to a plane. The set of possible transformations of these networks is restricted by the condition that at each step all the faces must be triangles. We employed two main approaches. In the first approach we grow the manifolds using various simples rules, which progressively had new triangles. In the other approach we relax the structure of large manifolds while keeping the number of triangles constant. The networks resulting from these evolving triangulations demonstrate several interesting features, unexpected in planar networks, such as small-world diameters and power-law degree distributions. Finally, we manipulate the topology of the manifolds by introducing wormholes. The presence of wormholes can change significantly the network structure, depending on the rate at which they are introduced. Remarkably, if we make wormholes at constant rate, the network’s diameter shows a sub-logarithmic growth with the number of nodes in the system.
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Bertagnolli, Giulia. "Modelling the process-driven geometry of complex networks." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/346519.

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Graphs are a great tool for representing complex physical and social systems, where the interactions among many units, from tens of animal species in a food-web, to millions of users in a social network, give rise to emergent, complex system behaviours. In the field of network science this representation, which is usually called a complex network, can be complicated at will to better represent the real system under study. For instance, interactions may be directed or may differ in their strength or cost, leading to directed weighted networks, but they may also depend on time, like in temporal networks, or nodes (i.e. the units of the system) may interact in different ways, in which case edge-coloured multi-graphs and multi-layer networks represent better the system. Besides this rich repertoire of network structures, we cannot forgot that edges represent interactions and that this interactions are not static, but are, instead, purposely established to reach some function of the system, as for instance, routing people and goods through a transportation network or cognition, through the exchange of neuro-physiological signals in the brain. Building on the foundations of spectral graph theory, of non-linear dimensionality reduction and diffusion maps, and of the recently introduced diffusion distance [Phys. Rev. Lett. 118, 168301 (2017)] we use the simple yet powerful tool of continuous-time Markov chains on networks to model their process-driven geometry and characterise their functional shape. The main results are: (i) the generalisation of the diffusion geometry framework to different types of interconnected systems (from edge-coloured multigraphs to multi-layer networks) and of random walk dynamics [Phys. Rev. E 103, 042301 (2021)] and (ii) the introduction of new descriptors based on the diffusion geometry to quantify and describe the micro- (through the network depth [J. Complex Netw. 8, 4 (2020)]), meso- (functional rich-club) and macro-scale (using statistics of the pairwise distances between the network's nodes [Comm. Phys. 4, 125 (2021)]) of complex networks.
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Haschke, Robert. "Bifurcations in discrete time neural networks : controlling complex network behaviour with inputs." kostenfrei, 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=973184663.

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Books on the topic "Complex network"

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In, Visarath, and Antonio Palacios. Symmetry in Complex Network Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-55545-3.

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Lu, Xin Biao. Synchronization in complex networks. New York: Nova Science Publisher's, 2011.

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service), SpringerLink (Online, ed. Valuation of Network Effects in Software Markets: A Complex Networks Approach. Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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1965-, Barthélemy Marc, and Vespignani Alessandro 1965-, eds. Dynamical processes on complex networks. Cambridge: Cambridge University Press, 2009.

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Xing, Lizhi. Complex Network-Based Global Value Chain Accounting System. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9264-2.

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Gomez-Pilar, Javier. Characterization of Neural Activity Using Complex Network Theory. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49900-6.

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Karamanos, Anastasios. Network embeddedness and the value of complex resources. Cambridge: ESRC Centre for Business Research, University of Cambridge, 2002.

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Ukkusuri, Satish V., and Kaan Ozbay, eds. Advances in Dynamic Network Modeling in Complex Transportation Systems. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6243-9.

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L, Kocarev, and Vattay G, eds. Complex dynamics in communication networks. Berlin ; New York: Springer, 2005.

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The structure of complex networks: Theory and applications. New York: Oxford University Press, 2012.

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Book chapters on the topic "Complex network"

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Fantoni, S. "Sustainability Complex Network." In Quantitative Sustainability, 3–26. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-39311-2_1.

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AbstractWe introduce the concept of quantitative evaluation of a complex network made up of researchers operating in different disciplines and different sectors belonging to life and hard sciences or social science and humanities or industrial and entrepreneurial activities, which, in addition to their disciplinary research, interact within each other in interdisciplinary scientific collaborations on sustainability projects. The complex network that we consider in this paper is of the small-world type, which has been already used in the study of several other biological, technological and social complex systems. This kind of network has a flexible structure which is in between those of the completely regular and the completely random networks. Similarly, the increase of interdisciplinary collaborations amongst scholars having a large and recognized experience in a given disciplinary sector may be favoured by random links arising in facing up specific issues of sustainability. Numerical results are given for a few unweighted networks having up to ten research groups with up to hundred researchers each.
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Ginsparg, Paul. "Scholarly Information Network." In Complex Networks, 313–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44485-5_15.

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Kamiński, Bogumił, Paweł Prałat, and François Théberge. "Network Robustness." In Mining Complex Networks, 239–50. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003218869-10.

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Ivanov, Plamen Ch, and Ronny P. Bartsch. "Network Physiology: Mapping Interactions Between Networks of Physiologic Networks." In Understanding Complex Systems, 203–22. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03518-5_10.

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Slingerland, Willeke. "Social Capital, Corrupt Networks, and Network Corruption." In Understanding Complex Systems, 9–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81484-7_2.

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Cook, Andrew, and Massimiliano Zanin. "Complex Network Theory." In Complexity Science in Air Traffic Management, 9–22. Burlington, VT : Ashgate, [2016] |: Routledge, 2016. http://dx.doi.org/10.4324/9781315573205-2.

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Brandes, Ulrik, Jürgen Lerner, Uwe Nagel, and Bobo Nick. "Structural Trends in Network Ensembles." In Complex Networks, 83–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01206-8_8.

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Farkas, Illés, Imre Derényi, Gergely Palla, and Tamás Vicsek. "Equilibrium Statistical Mechanicsof Network Structures." In Complex Networks, 163–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44485-5_8.

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Kuikka, Vesa. "Subsystem Cooperation in Complex Networks - Case Brain Network." In Complex Networks XI, 156–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40943-2_14.

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Estrada, Ernesto, Maria Fox, Desmond J. Higham, and Gian-Luca Oppo. "Complex Networks: An Invitation." In Network Science, 1–11. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-396-1_1.

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Conference papers on the topic "Complex network"

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Matsumoto, Masakazu, Akinori Baba, Iwao Ohmine, Michio Tokuyama, Irwin Oppenheim, and Hideya Nishiyama. "Network Motif of Water." In COMPLEX SYSTEMS: 5th International Workshop on Complex Systems. AIP, 2008. http://dx.doi.org/10.1063/1.2897788.

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Haley, Brandon M., Andy Dong, and Irem Y. Tumer. "Creating Faultable Network Models of Complex Engineered Systems." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34407.

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This paper presents a new methodology for modeling complex engineered systems using complex networks for failure analysis. Many existing network-based modeling approaches for complex engineered systems “abstract away” the functional details to focus on the topological configuration of the system and thus do not provide adequate insight into system behavior. To model failures more adequately, we present two types of network representations of a complex engineered system: a uni-partite architectural network and a weighted bi-partite behavioral network. Whereas the architectural network describes physical inter-connectivity, the behavioral network represents the interaction between functions and variables in mathematical models of the system and its constituent components. The levels of abstraction for nodes in both network types affords the evaluation of failures involving morphology or behavior, respectively. The approach is shown with respect to a drivetrain model. Architectural and behavioral networks are compared with respect to the types of faults that can be described. We conclude with considerations that should be employed when modeling complex engineered systems as networks for the purpose of failure analysis.
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Yang, Chun-Lin, and C. Steve Suh. "On the Proper Description of Complex Network Dynamics." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88051.

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Real-world networks are dynamical complex network systems. The dynamics of a network system is a coupling of the local dynamics with the global dynamics. The local dynamics is the time-varying behaviors of ensembles at the local level. The global dynamics is the collective behavior of the ensembles following specific laws at the global level. These laws include basic physical principles and constraints. Complex networks have inherent resilience that offsets disturbance and maintains the state of the system. However, when disturbance is potent enough, network dynamics can be perturbed to a level that ensembles no longer follow the constraint conditions. As a result, the collective behavior of a complex network diminishes and the network collapses. The characteristic of a complex network is the response of the system which is time-dependent. Therefore, complex networks need to account for time-dependency and obey physical laws and constraints. Statistical mechanics is viable for the study of multi-body dynamic systems having uncertain states such as complex network systems. Statistical entropy can be used to define the distribution of the states of ensembles. The difference between the states of ensembles define the interaction between them. This interaction is known as the collective behavior. In other words statistical entropy defines the dynamics of a complex network. Variation of entropy corresponds to the variation of network dynamics and vice versa. Therefore, entropy can serve as an indicator of network dynamics. A stable network is characterized by a specific entropy while a network on the verge of collapse is characterized by another. As the collective behavior of a complex network can be described by entropy, the correlation between the statistical entropy and network dynamics is investigated.
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Yang, Chun-Lin, and C. Steve Suh. "On the Dynamics of Complex Network." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71994.

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Controlling complex network systems is challenging because network systems are highly coupled by ensembles and behaving with uncertainty. A network is composed by nodes and edges. Edges serve as the connection between nodes to exchange state information and further achieve state consensus. Through edges, the dynamics of individual nodes at the local level intimately affects the network dynamics at the global level. As a following bird can occasionally lose visual contact with the target bird in a flock at any moment, the edge between two nodes in a real world network systems is not necessarily always intact. Contrary to common sense, these real-world networks are usually perfectly stable even when the edges between the nodes are unstable. This suggests that not only nodes are dynamical, edges are dynamical, too. Since the edges between the nodes are changing dynamically, network configuration is also dynamical. Further, edges need be defined and quantified so that the unstable connection behavior can be properly described. The paper explores the concepts of statistical mechanics and statistical entropy to address the particular need. Statistical mechanics describes the behavior of a mechanical system that has uncertain states. Statistical entropy on the other hand defines the distribution of the microstates by probability. Entropy provides a measure of the level of network integrity. With entropy, one can assign desired dynamics to the network to ensure desired network property. This work aims to construct a complex network structure model based on the edge dynamics. Coupled with node self-dynamic and consensus law, a general dynamical network model can be constructed.
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Small, Michael, Kevin Judd, and Linjun Zhang. "How is that complex network complex?" In 2014 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2014. http://dx.doi.org/10.1109/iscas.2014.6865372.

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Wlassova, L. N. "Network data base of physical technologies." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386859.

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Ferreira, Kecia A. M., Mariza A. S. Bigonha, Roberto S. Bigonha, and Bárbara M. Gomes. "Software Evolution Characterization - A Complex Network Approach." In Simpósio Brasileiro de Qualidade de Software. Sociedade Brasileira de Computação - SBC, 2011. http://dx.doi.org/10.5753/sbqs.2011.15386.

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Software evolution has been the subject of research in the last decades, revealing that a software system has continuing growth, continuing changes, increasing complexity and declining quality. However, the knowledge about how this process occurs is not consolidate yet. This paper presents the results of a study about software evolution characterization based on concepts of Complex Networks. We analyzed 16 open-source software systems and one commercial application, in a total of 129 versions. The results of this study show that: the density of a software network decreases as the software system grows; the diameter of such networks is short; the classes with higher in-degree keep this status; such classes are unstable and their internal cohesion degrades. Our investigations also revealed an interesting picture which models the macroscopic structure of software networks. We called it the little house.
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Qin, Lang, Ling Zhou, and Jar-Der Luo. "Complex network perspective on network dynamics." In the First ACM International Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2392622.2392632.

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Melo, Renato Silva, and André Luís Vignatti. "Preprocessing Rules for Target Set Selection in Complex Networks." In Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/brasnam.2020.11167.

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In the Target Set Selection (TSS) problem, we want to find the minimum set of individuals in a network to spread information across the entire network. This problem is NP-hard, so find good strategies to deal with it, even for a particular case, is something of interest. We introduce preprocessing rules that allow reducing the size of the input without losing the optimality of the solution when the input graph is a complex network. Such type of network has a set of topological properties that commonly occurs in graphs that model real systems. We present computational experiments with real-world complex networks and synthetic power law graphs. Our strategies do particularly well on graphs with power law degree distribution, such as several real-world complex networks. Such rules provide a notable reduction in the size of the problem and, consequently, gains in scalability.
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Jian, Han, and Feng Ye. "Complex Network Flexibility Discussion." In 2013 International Conference on Advanced ICT. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/icaicte.2013.164.

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Reports on the topic "Complex network"

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Bailey, D. J. Nuclear weapons complex network management overview. Office of Scientific and Technical Information (OSTI), April 1989. http://dx.doi.org/10.2172/6295252.

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Warnick, Sean, and Daniel Zappala. Analysis and Design of Complex Network Environments. Fort Belvoir, VA: Defense Technical Information Center, February 2014. http://dx.doi.org/10.21236/ada596289.

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Warnick, Sean, and Daniel Zappala. Analysis and Design of Complex Network Environments. Fort Belvoir, VA: Defense Technical Information Center, March 2012. http://dx.doi.org/10.21236/ada557240.

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DeMar, Phil. Complex Network Analysis and Intelligent Monitoring Platform. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1827370.

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Soloviev, Vladimir, Victoria Solovieva, Anna Tuliakova, Alexey Hostryk, and Lukáš Pichl. Complex networks theory and precursors of financial crashes. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4119.

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Based on the network paradigm of complexity in the work, a systematic analysis of the dynamics of the largest stock markets in the world and cryptocurrency market has been carried out. According to the algorithms of the visibility graph and recurrence plot, the daily values of stock and crypto indices are converted into a networks and multiplex networks, the spectral and topological properties of which are sensitive to the critical and crisis phenomena of the studied complex systems. This work is the first to investigate the network properties of the crypto index CCI30 and the multiplex network of key cryptocurrencies. It is shown that some of the spectral and topological characteristics can serve as measures of the complexity of the stock and crypto market, and their specific behaviour in the pre-crisis period is used as indicators- precursors of critical phenomena.
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Parekh, Ojas D., Jeremy D. Wendt, Luke Shulenburger, Andrew J. Landahl, Jonathan Edward Moussa, and John B. Aidun. Benchmarking Adiabatic Quantum Optimization for Complex Network Analysis. Office of Scientific and Technical Information (OSTI), April 2015. http://dx.doi.org/10.2172/1459086.

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Goldsmith, Andrea J., Stephen Boyd, H. V. Poor, and Yonina Eldar. Complex Network Information Exchange in Random Wireless Environments. Fort Belvoir, VA: Defense Technical Information Center, June 2012. http://dx.doi.org/10.21236/ada576751.

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Delgado Alonso, Jesus. Geochemical Monitoring System and Network for Complex Subsurface Matrices. Office of Scientific and Technical Information (OSTI), December 2019. http://dx.doi.org/10.2172/1576600.

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Gureck, W. S. Network Centric Warfare and Complex Humanitarian Emergencies, Meet Napster! Fort Belvoir, VA: Defense Technical Information Center, February 2001. http://dx.doi.org/10.21236/ada389857.

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Bielinskyi, Andrii O., and Vladimir N. Soloviev. Complex network precursors of crashes and critical events in the cryptocurrency market. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2881.

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This article demonstrates the possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency. For this purpose, the methods of the theory of complex networks have been used. The possibility of constructing dynamic measures of network complexity behaving in a proper way during actual pre-crash periods has been shown. This fact is used to build predictors of crashes and critical events phenomena on the examples of all the patterns recorded in the time series of the key cryptocurrency Bitcoin, the effectiveness of the proposed indicators-precursors of these falls has been identified.
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