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Articles de revues sur le sujet "COMPLES NETWORK"
PELLEGRINI, Lilla, Monica LEBA et Alexandru IOVANOVICI. « CHARACTERIZATION OF URBAN TRANSPORTATION NETWORKS USING NETWORK MOTIFS ». Acta Electrotechnica et Informatica 20, no 4 (21 janvier 2020) : 3–9. http://dx.doi.org/10.15546/aeei-2020-0019.
Texte intégralTarapata, Zbigniew. « Modelling and analysis of transportation networks using complex networks : Poland case study ». Archives of Transport 36, no 4 (31 décembre 2015) : 55–65. http://dx.doi.org/10.5604/08669546.1185207.
Texte intégralAsbaş, Caner, Zühal Şenyuva et Şule Tuzlukaya. « New Organizations in Complex Networks : Survival and Success ». Central European Management Journal 30, no 1 (15 mars 2022) : 11–39. http://dx.doi.org/10.7206/cemj.2658-0845.68.
Texte intégralHu, Ziping, Krishnaiyan Thulasiraman et 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.
Texte intégralMaciá-Pérez, Francisco, Iren Lorenzo-Fonseca, Jose Vicente Berná-Martinez et 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.
Texte intégralXu, Shuai, et Bai Da Zhang. « Complex Network Model and its Application ». Advanced Materials Research 791-793 (septembre 2013) : 1589–92. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1589.
Texte intégralHernandez, Bryan S., Patrick Vincent N. Lubenia, Matthew D. Johnston et Jae Kyoung Kim. « A framework for deriving analytic steady states of biochemical reaction networks ». PLOS Computational Biology 19, no 4 (13 avril 2023) : e1011039. http://dx.doi.org/10.1371/journal.pcbi.1011039.
Texte intégralGuo, Dong Wei, Xiang Yan Meng et Cai Fang Hou. « Building Complex Network Similar to Facebook ». Applied Mechanics and Materials 513-517 (février 2014) : 909–13. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.909.
Texte intégralKoam, Ali N. A., Ali Ahmad et Yasir Ahmad. « Computation of reverse degree-based topological indices of hex-derived networks ». AIMS Mathematics 6, no 10 (2021) : 11330–45. http://dx.doi.org/10.3934/math.2021658.
Texte intégralSivakumar, B., et F. M. Woldemeskel. « Complex networks for streamflow dynamics ». Hydrology and Earth System Sciences 18, no 11 (20 novembre 2014) : 4565–78. http://dx.doi.org/10.5194/hess-18-4565-2014.
Texte intégralThèses sur le sujet "COMPLES NETWORK"
HANOT, RAHUL. « COMMUNITY DTECTION USING FIRE PROPAGATION AND BOUNDARY VERTICES ALGORITHMS ». Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18779.
Texte intégralKleineberg, Kaj Kolja. « Evolution and ecology of the digital world : A complex systems perspective ». Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/400404.
Texte intégralEsta tesis está dedicada a los retos de un mundo interconectado que ha emergido a partir de la reciente revolución digital en el que servicios digitales se desarrollan y compiten en la ausencia de control central. Por tanto, herramientas, ideas y técnicas de análisis de sistemas complejos son útiles y especialmente adecuadas para describir la evolución y competencia entre redes sociales online. El éxito del Internet ha conectado individuos a escalas sin precedentes y la Web 2.0 promociona hoy en día colaboración global y el intercambio de ideas casi instantáneo. Sin embargo, la dominación de unos pocos poderosos monopolios de información representa un peligro para la libertad de ideas y decisiones de individuos. Por tanto, dos factores son esenciales para un futuro próspero en la era digital: diversidad digital y decentralización. En cuanto al primero, hemos introducido modelos basados en observaciones empíricas que permiten entender mejor la dinámica y las interacciones competitivas de las redes sociales online, los sistemas claves en el cosmos de la Web 2.0. En particular, nuestros descubrimientos revelan las condiciones en las cuales la diversidad digital se puede sostener. Con respecto al segundo, el diseño de arquitecturas descentralizadas contiene retos específicos, entre los que nos dirigimos especialmente a la búsqueda y navegación basada exclusivamente en conocimiento local. Hemos revelado en qué condiciones la existencia de muchas redes interaccionando facilita estas tareas. Afortunadamente, muchos sistemas reales cumplen estas condiciones. Para concluir, desde una perspectiva a nivel de sistema, un futuro próspero en el mundo digital compuesto por un paisaje digital diverso con arquitecturas descentralizadas en constante interacción es posible, pero no seguro. En esta situación, la conciencia, así como la creación de los incentivos adecuados, son retos importantes que nuestra sociedad debe afrontar. Crear conciencia suficiente e incentivos correctos para crear ese futuro sigue siendo un reto para la sociedad.
Lordan, Oriol. « Airline route networks : a complex network approach ». Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/144526.
Texte intégralPaula, DemÃtrius Ribeiro de. « Dynamics of neural networks and cluster growth in complex networks ». Universidade Federal do CearÃ, 2006. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=206.
Texte intégralEste dissertaÃÃo foi dividida em duas partes, na primeira parte nÃs propomos um modelo de crescimento competitivo de gregados em redes complexas para simular a propagaÃÃo de idÃias ou opiniÃes em comunidades. Investigamos como as distribuiÃÃes de tamanhos de agregados variam com a topologia de construÃÃo da rede e com o nÃmero de sementes aleatoriamente dispersas na estrutura. Para tal, analisamos redes do tipo de Erdos-RÃnyi, redes de contato preferencial e a chamada rede Apoloniana. Esta Ãltima apresenta distribuiÃÃes de tamanho de agregado em forma de uma lei de potÃncia com um expoente aproximadamente 1. Resultados similares sÃo observados com as distribuiÃÃes obtidas para as fraÃÃes de votos por candidato Ãs eleiÃÃes proporcionais para deputados no Brasil. Na segunda parte, analisamos o comportamento temporal da atividade neural em redes com caracterÃsticas de mundo pequeno e em redes construÃdas segundo o modelo do contato preferencial. Nesta primeira topologia, estudamos como a sÃrie temporal se comporta com a variaÃÃo do alcance das conexÃes. Em ambas as topologias, observamos a formaÃÃo de perÃodos e investigamos como estes variam com o tamanho da rede.
The process by which news trends and ideas propagate in social communities can have a profound impact in the life of individuals. To understand thi process, we introduce a competitive cluster growth model in complex networks. In our model, each cluster represents the set of individuals with a certain opinion or preference. We investigate how the cluster size distribution depends on the topology of the network and how it is affected by the number of initial seeds dispersed in the structure. We study our model using different network models, namely, the Erdos-Renyi geometry, the preferential attachment model, and the so-called Apollonian network. This last complex geometry displays a cluster size distribution that follows a power-law with an exponent 1.0. Similar results have been obtained for the distributions of number of votes per candidate in the proportional elections for federal representation in Brazil. In the second part of this work, we investigate the temporal behavior of neural networks with small world topology and in networks built according to the preferential attachment model. In the first case we study the effect of the range of connections on the behavior of the time series. In both topologies, we detect the existence of cycles and investigate how their periods depend on the size of the system.
Reis, Saulo-Davi Soares e. « Navegação em redes espacialmente correlacionadas ». reponame:Repositório Institucional da UFC, 2009. http://www.repositorio.ufc.br/handle/riufc/12888.
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A significant number of real networks have well-defined spatial characteristics. We studied how network with spatially correlated topolgies can influence the processes of navigation through them. For this, we study the behavior of the average shortest-path length to networks defined within Kleinberg’s model [1, 2] to analyze the navigation dictated by rules of global knowledge. The Kleinberg’s model is characterized by allowing long-range connections between two vertices u and v distributed by a power-law probability distribution. For a better understanding of the topological characteristics presented by this family of networks, we applied the epidemic model susceptible-infected-susceptible (SIS) and we found that, we see that the Kleinberg’s model presents the small-world phenomenon only for a certain range of values of the clustering exponent α. We introduced a model of spatially embedded networks, conceptually based on the Kleinberg’s model. This model consist in introduction of a constrain to the distribution of long-range connections. We associate his constrain to a possible cost involved in the process of adding new long-range connections to the network. We studied how this cost constrain affects the navigation through the system, taking as a basis for comparison the work of Kleinberg for navigation with local knowledge, and our results conserning for navigation with global knowledge.
Um número significativo de redes reais apresentam características espaciais bem definidas. Nós estudamos como topologias de redes espacialmente correlacionadas podem influenciar processos de navegação através das mesmas. Para isso estudamos o comportamento do mínimo caminho médio para redes definidas dentro de modelo de Kleinberg para analisar a navegação ditada por regras de conhecimento global. O modelo que Kleinberg caracteriza-se por permitir conexões de longo alcance entre dois vértices u e v distribuídas por uma distribuição de probabilidade em lei de potência. Para um melhor entendimento das características topológicas apresentadas por essa família de redes, nós aplicamos o modelo epidêmico suscetível-infectado-suscetível (SIS), e com isso verificamos que o modelo de Kleinberg apresenta fenômeno de mundo pequeno apenas para uma determinada faixa de valores assumidos pelo expoente de agregação α. Em seguida, introduzimos um modelo de redes espacialmente embutidas, conceitualmente inspirado no modelo de Kleinberg. Este traduz-se na introdução de um vínculo para a distribuição das conexões de longo alcance. Associamos este vínculo a um possível custo envolvido no processo de adição de novas conexões de longo alcance à rede. Estudamos como esse vínculo no custo afeta a navegação na rede, tendo como base de comparação os trabalhos de Kleinberg para a navegação com conhecimento local da topologia, e nossos resultados considerando a navegação com conhecimento global.
Khorramzadeh, Yasamin. « Network Reliability : Theory, Estimation, and Applications ». Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/64383.
Texte intégralPh. D.
Reis, Elohim Fonseca dos 1984. « Criticality in neural networks = Criticalidade em redes neurais ». [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276917.
Texte intégralDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin
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Resumo: Este trabalho é dividido em duas partes. Na primeira parte, uma rede de correlação é construída baseada em um modelo de Ising em diferentes temperaturas, crítica, subcrítica e supercrítica, usando um algorítimo de Metropolis Monte-Carlo com dinâmica de \textit{single-spin-flip}. Este modelo teórico é comparado com uma rede do cérebro construída a partir de correlações das séries temporais do sinal BOLD de fMRI de regiões do cérebro. Medidas de rede, como coeficiente de aglomeração, mínimo caminho médio e distribuição de grau são analisadas. As mesmas medidas de rede são calculadas para a rede obtida pelas correlações das séries temporais dos spins no modelo de Ising. Os resultados da rede cerebral são melhor explicados pelo modelo teórico na temperatura crítica, sugerindo aspectos de criticalidade na dinâmica cerebral. Na segunda parte, é estudada a dinâmica temporal da atividade de um população neural, ou seja, a atividade de células ganglionares da retina gravadas em uma matriz de multi-eletrodos. Vários estudos têm focado em descrever a atividade de redes neurais usando modelos de Ising com desordem, não dando atenção à estrutura dinâmica. Tratando o tempo como uma dimensão extra do sistema, a dinâmica temporal da atividade da população neural é modelada. O princípio de máxima entropia é usado para construir um modelo de Ising com interação entre pares das atividades de diferentes neurônios em tempos diferentes. O ajuste do modelo é feito com uma combinação de amostragem de Monte-Carlo e método do gradiente descendente. O sistema é caracterizado pelos parâmetros aprendidos, questões como balanço detalhado e reversibilidade temporal são analisadas e variáveis termodinâmicas, como o calor específico, podem ser calculadas para estudar aspectos de criticalidade
Abstract: This work is divided in two parts. In the first part, a correlation network is build based on an Ising model at different temperatures, critical, subcritical and supercritical, using a Metropolis Monte-Carlo algorithm with single-spin-flip dynamics. This theoretical model is compared with a brain network built from the correlations of BOLD fMRI temporal series of brain regions activity. Network measures, such as clustering coefficient, average shortest path length and degree distributions are analysed. The same network measures are calculated to the network obtained from the time series correlations of the spins in the Ising model. The results from the brain network are better explained by the theoretical model at the critical temperature, suggesting critical aspects in the brain dynamics. In the second part, the temporal dynamics of the activity of a neuron population, that is, the activity of retinal ganglion cells recorded in a multi-electrode array was studied. Many studies have focused on describing the activity of neural networks using disordered Ising models, with no regard to the dynamic nature. Treating time as an extra dimension of the system, the temporal dynamics of the activity of the neuron population is modeled. The maximum entropy principle approach is used to build an Ising model with pairwise interactions between the activities of different neurons at different times. Model fitting is performed by a combination of Metropolis Monte Carlo sampling with gradient descent methods. The system is characterized by the learned parameters, questions like detailed balance and time reversibility are analysed and thermodynamic variables, such as specific heat, can be calculated to study critical aspects
Mestrado
Física
Mestre em Física
2013/25361-6
FAPESP
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.
Texte intégralHollingshad, Nicholas W. « A Non-equilibrium Approach to Scale Free Networks ». Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc149609/.
Texte intégralRocha, Luis Enrique Correa da. « Redes acopladas : estrutura e dinâmica ». Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-11092007-183106/.
Texte intégralComplex network theory has become very popular because of its interdisciplinarity, conceptual simplicity and wide applicability to model real systems. Although fast growing, there is a number of problems which have not been addressed by using complex networks. For example, few efforts have been directed to systems involving coupling and interaction between different complex networks. In the following monography, we present two fundamental contributions in the study of such systems. The first consists in a model which describes the interaction dynamics between a mass pattern evolving in a regular network with a complex network, which are expected to control the pattern evolution. As soon as a complex network node is activated by the regular network, it requests help from its topological neighbours and activates them. The activation is triggered when the mass concentration overcomes a threshold in the node position and consists in liberating an opposite diffusion intended to eliminate the original pattern. The dynamics is completely related to the structure of the control network. The existence of hubs in the Barabási-Albert model plays a fundamental role to accelerate the opposite mass generation. Conversely, the uniform distribution of neighbours in the Erdös-Rényi network provided an increase in the efficiency when several focuses of the original pattern were distributed in the regular network. The second contribution consists in a model based on interactions between two species (predator and prey) provided by sensitive fields which depends of the Euclidean distance between two agents and on their respective types. Spatio-temporal patterns emerge in the system which are directly related to the attraction intensity between same species agents. To understand the dynamics evolution and quantify the information transfer through different clusters, we built two complex networks where the nodes represent the agents. In the first network, the edge weight is given by the Euclidean distance between two agents and, in the second network, by the amount of time two agents become close one another. By following a merging process, another network is obtained whose nodes correspond to spatial groups defined by a weight thresholding process in the first network. Some configurations favor the preys survival, while predators efficiency are improved by other ones.
Livres sur le sujet "COMPLES NETWORK"
da F. Costa, Luciano, Alexandre Evsukoff, Giuseppe Mangioni et Ronaldo Menezes, dir. Complex Networks. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25501-4.
Texte intégralMenezes, Ronaldo, Alexandre Evsukoff et Marta C. González, dir. Complex Networks. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30287-9.
Texte intégralFortunato, Santo, Giuseppe Mangioni, Ronaldo Menezes et Vincenzo Nicosia, dir. Complex Networks. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01206-8.
Texte intégralBen-Naim, Eli, Hans Frauenfelder et Zoltan Toroczkai, dir. Complex Networks. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b98716.
Texte intégralMenezes, Ronaldo. Complex Networks. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013.
Trouver le texte intégralservice), SpringerLink (Online, dir. Valuation of Network Effects in Software Markets : A Complex Networks Approach. Heidelberg : Springer-Verlag Berlin Heidelberg, 2010.
Trouver le texte intégralTeixeira, Andreia Sofia, Diogo Pacheco, Marcos Oliveira, Hugo Barbosa, Bruno Gonçalves et Ronaldo Menezes, dir. Complex Networks XII. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81854-8.
Texte intégralContucci, Pierluigi, Ronaldo Menezes, Andrea Omicini et Julia Poncela-Casasnovas, dir. Complex Networks V. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05401-8.
Texte intégralCornelius, Sean P., Clara Granell Martorell, Jesús Gómez-Gardeñes et Bruno Gonçalves, dir. Complex Networks X. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14459-3.
Texte intégralGhoshal, Gourab, Julia Poncela-Casasnovas et Robert Tolksdorf, dir. Complex Networks IV. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36844-8.
Texte intégralChapitres de livres sur le sujet "COMPLES NETWORK"
Ivanov, Plamen Ch, et Ronny P. Bartsch. « Network Physiology : Mapping Interactions Between Networks of Physiologic Networks ». Dans Understanding Complex Systems, 203–22. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03518-5_10.
Texte intégralSlingerland, Willeke. « Social Capital, Corrupt Networks, and Network Corruption ». Dans Understanding Complex Systems, 9–27. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81484-7_2.
Texte intégralKuikka, Vesa. « Subsystem Cooperation in Complex Networks - Case Brain Network ». Dans Complex Networks XI, 156–69. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40943-2_14.
Texte intégralLi, Deyi. « Complex Networks and Network Data Mining ». Dans Database Systems for Advanced Applications, 3. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11408079_3.
Texte intégralKenett, Dror Y., Jianxi Gao, Xuqing Huang, Shuai Shao, Irena Vodenska, Sergey V. Buldyrev, Gerald Paul, H. Eugene Stanley et Shlomo Havlin. « Network of Interdependent Networks : Overview of Theory and Applications ». Dans Understanding Complex Systems, 3–36. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03518-5_1.
Texte intégralAdiga, Abhijin, Hilton Galyean, Chris J. Kuhlman, Michael Levet, Henning S. Mortveit et Sichao Wu. « Network Structure and Activity in Boolean Networks ». Dans Cellular Automata and Discrete Complex Systems, 210–23. Berlin, Heidelberg : Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47221-7_16.
Texte intégralSilva, Miguel E. P., Pedro Paredes et Pedro Ribeiro. « Network Motifs Detection Using Random Networks with Prescribed Subgraph Frequencies ». Dans Complex Networks VIII, 17–29. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54241-6_2.
Texte intégralMelançon, Guy, Benjamin Renoust et Haolin Ren. « Handling Complex Multilayer Networks—An Approach Based on Visual Network Analytics ». Dans Understanding Complex Systems, 51–70. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59302-5_3.
Texte intégralGinsparg, Paul. « Scholarly Information Network ». Dans Complex Networks, 313–36. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-44485-5_15.
Texte intégralKamiński, Bogumił, Paweł Prałat et François Théberge. « Network Robustness ». Dans Mining Complex Networks, 239–50. Boca Raton : Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003218869-10.
Texte intégralActes de conférences sur le sujet "COMPLES NETWORK"
Semenets, Valerii, Valeriia Chumak, Iryna Svyd, Oleg Zubkov, Oleksandr Vorgul et Natalia Boiko. « DESIGNING THE STRUCTURE OF A GENERAL-PURPOSE TELEMEDICINE COMPLEX ». Dans 2021 III International Scientific and Practical Conference Theoretical and Applied Aspects of Device Development on Microcontrollers and FPGAs. MC-ampFPGA-2021, 2021. http://dx.doi.org/10.35598/mcfpga.2021.016.
Texte intégralSha, Zhenghui, et Jitesh H. Panchal. « A Degree-Based Decision-Centric Model for Complex Networked Systems ». Dans ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60036.
Texte intégralHaley, Brandon M., Andy Dong et Irem Y. Tumer. « Creating Faultable Network Models of Complex Engineered Systems ». Dans 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.
Texte intégralYang, Chun-Lin, et C. Steve Suh. « On the Proper Description of Complex Network Dynamics ». Dans ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88051.
Texte intégralKupershtein, Leonid M., Mykhailo D. Krentsin et Andrii V. Prytula. « Use of peer-to-peer networks for secured communication ». Dans 16th IC Measurement and Control in Complex Systems. Vinnytsia : VNTU, 2022. http://dx.doi.org/10.31649/mccs2022.20.
Texte intégralSha, Zhenghui, et Jitesh H. Panchal. « Estimating the Node-Level Behaviors in Complex Networks From Structural Datasets ». Dans ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12063.
Texte intégralLouri, Ahmed, Hongki Sung, Yoonkeon Moon et Bernard P. Zeigler. « An Efficient Signal Distinction Scheme for Large-scale Free-space Optical Networks Using Genetic Algorithms ». Dans Photonics in Switching. Washington, D.C. : Optica Publishing Group, 1995. http://dx.doi.org/10.1364/ps.1995.pthc5.
Texte intégralMelo, Renato Silva, et André Luís Vignatti. « Preprocessing Rules for Target Set Selection in Complex Networks ». Dans Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/brasnam.2020.11167.
Texte intégralLilian Huang, Meng Gao et Guanghui Sun. « Complete synchronization in weighted complex networks ». Dans 2010 3rd International Symposium on Systems and Control in Aeronautics and Astronautics (ISSCAA 2010). IEEE, 2010. http://dx.doi.org/10.1109/isscaa.2010.5632365.
Texte intégralLezhniuk, Petro D., et Vladyslav M. Lysyi. « Assessment of the impact of factors which influence the energy efficiency of res during the balancing of electrical energy system modes ». Dans 16th IC Measurement and Control in Complex Systems. Vinnytsia : VNTU, 2022. http://dx.doi.org/10.31649/mccs2022.11.
Texte intégralRapports d'organisations sur le sujet "COMPLES NETWORK"
Soloviev, Vladimir, Victoria Solovieva, Anna Tuliakova, Alexey Hostryk et Lukáš Pichl. Complex networks theory and precursors of financial crashes. [б. в.], octobre 2020. http://dx.doi.org/10.31812/123456789/4119.
Texte intégralKleinberg, Robert D. Kleinberg Complex Networks. Fort Belvoir, VA : Defense Technical Information Center, octobre 2014. http://dx.doi.org/10.21236/ada612226.
Texte intégralLai, Ying-Cheng. Security of Complex Networks. Fort Belvoir, VA : Defense Technical Information Center, février 2010. http://dx.doi.org/10.21236/ada567229.
Texte intégralNechaev, V., Володимир Миколайович Соловйов et A. Nagibas. Complex economic systems structural organization modelling. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1118.
Texte intégralBailey, D. J. Nuclear weapons complex network management overview. Office of Scientific and Technical Information (OSTI), avril 1989. http://dx.doi.org/10.2172/6295252.
Texte intégralLai, Ying C. Predicting and Controlling Complex Networks. Fort Belvoir, VA : Defense Technical Information Center, juin 2015. http://dx.doi.org/10.21236/ada619238.
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