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Libros sobre el tema "COMPLES NETWORK"

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Publicado: 11 de septiembre de 2023

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1

da F. Costa, Luciano, Alexandre Evsukoff, Giuseppe Mangioni y Ronaldo Menezes, eds. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25501-4.

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Menezes, Ronaldo, Alexandre Evsukoff y Marta C. González, eds. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30287-9.

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3

Fortunato, Santo, Giuseppe Mangioni, Ronaldo Menezes y Vincenzo Nicosia, eds. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01206-8.

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4

Ben-Naim, Eli, Hans Frauenfelder y Zoltan Toroczkai, eds. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b98716.

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5

Menezes, Ronaldo. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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6

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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7

Teixeira, Andreia Sofia, Diogo Pacheco, Marcos Oliveira, Hugo Barbosa, Bruno Gonçalves y Ronaldo Menezes, eds. Complex Networks XII. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81854-8.

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8

Contucci, Pierluigi, Ronaldo Menezes, Andrea Omicini y Julia Poncela-Casasnovas, eds. Complex Networks V. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05401-8.

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9

Cornelius, Sean P., Clara Granell Martorell, Jesús Gómez-Gardeñes y Bruno Gonçalves, eds. Complex Networks X. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14459-3.

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10

Ghoshal, Gourab, Julia Poncela-Casasnovas y Robert Tolksdorf, eds. Complex Networks IV. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36844-8.

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11

Göknar, İzzet Cem y Levent Sevgi, eds. Complex Computing-Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-30636-6.

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12

Barbosa, Hugo, Jesus Gomez-Gardenes, Bruno Gonçalves, Giuseppe Mangioni, Ronaldo Menezes y Marcos Oliveira, eds. Complex Networks XI. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40943-2.

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13

Mangioni, Giuseppe, Filippo Simini, Stephen Miles Uzzo y Dashun Wang, eds. Complex Networks VI. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16112-9.

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14

Cornelius, Sean, Kate Coronges, Bruno Gonçalves, Roberta Sinatra y Alessandro Vespignani, eds. Complex Networks IX. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73198-8.

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15

Gonçalves, Bruno, Ronaldo Menezes, Roberta Sinatra y Vinko Zlatic, eds. Complex Networks VIII. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54241-6.

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16

Cherifi, Hocine, Bruno Gonçalves, Ronaldo Menezes y Roberta Sinatra, eds. Complex Networks VII. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30569-1.

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17

Pacheco, Diogo, Andreia Sofia Teixeira, Hugo Barbosa, Ronaldo Menezes y Giuseppe Mangioni, eds. Complex Networks XIII. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17658-6.

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18

Teixeira, Andreia Sofia, Federico Botta, José Fernando Mendes, Ronaldo Menezes y Giuseppe Mangioni, eds. Complex Networks XIV. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28276-8.

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19

In, Visarath y 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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20

1963-, Hirose Akira, ed. Complex-valued neural networks: Theories and applications. River Edge, NJ: World Scientific, 2003.

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21

Hirose, Akira. Complex-Valued Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27632-3.

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22

Commendatore, Pasquale, Mariano Matilla-García, Luis M. Varela y Jose S. Cánovas, eds. Complex Networks and Dynamics. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40803-3.

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23

Lü, Jinhu, Xinghuo Yu, Guanrong Chen y Wenwu Yu, eds. Complex Systems and Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-47824-0.

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24

Hirose, Akira. Complex-Valued Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-33457-6.

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25

Hirose, Akira, ed. Complex-Valued Neural Networks. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118590072.

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26

Chen, Guanrong, Xiaofan Wang y Xiang Li. Fundamentals of Complex Networks. Singapore: John Wiley & Sons Singapore Pte. Ltd, 2014. http://dx.doi.org/10.1002/9781118718124.

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27

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

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28

Structure in complex networks. Berlin: Springer, 2009.

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29

Lectures on complex networks. Oxford: Oxford University Press, 2010.

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30

Hirose, Akira. Complex-Valued Neural Networks. 2a ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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31

Pardalos, P. M. (Panos M.), 1954- y SpringerLink (Online service), eds. Handbook of Optimization in Complex Networks: Theory and Applications. Boston, MA: Springer US, 2012.

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32

Bianconi, Ginestra. Complex Systems as Multilayer Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0001.

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Chapter 1 constitutes Part I of the book: ‘Single and Multilayer Networks’. This chapter introduces multilayer networks as an important new development of Network Science that allows a more comprehensive understanding of Complex Systems. It identifies the main motivations driving the research activity in this field of multilayer networks and emphasizes the benefits of taking a multilayer network perpective to characterize network data. The main advantages of a multilayer network approach with respect to the more traditional single layer characterization of complex networks are broadly discussed, focusing on the information gain resulting from the analysis of multilayer networks, the non-reducibility of a multilayer network to a large single network and the rich interplay between structure and function in multilayer networks.
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33

Tan, Chee Wei y YU Pei-duo. Network Inference for Cyber Security in Complex Networks. Springer, 2020.

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34

Vernizzi, Graziano y Henri Orland. Complex networks. Editado por Gernot Akemann, Jinho Baik y Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.43.

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This article deals with complex networks, and in particular small world and scale free networks. Various networks exhibit the small world phenomenon, including social networks and gene expression networks. The local ordering property of small world networks is typically associated with regular networks such as a 2D square lattice. The small world phenomenon can be observed in most scale free networks, but few small world networks are scale free. The article first provides a brief background on small world networks and two models of scale free graphs before describing the replica method and how it can be applied to calculate the spectral densities of the adjacency matrix and Laplacian matrix of a scale free network. It then shows how the effective medium approximation can be used to treat networks with finite mean degree and concludes with a discussion of the local properties of random matrices associated with complex networks.
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35

Erciyes, Kayhan. Complex Networks. CRC Press, 2014. http://dx.doi.org/10.1201/b17409.

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36

Evsukoff, Alexandre, Ronaldo Menezes y Marta C. González. Complex Networks. Springer, 2014.

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37

Complex Networks. Springer Berlin / Heidelberg, 2010.

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38

Complex Networks. Springer, 2012.

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39

Erciyes, Kayhan. Complex Networks. Taylor & Francis Group, 2014.

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40

Giuseppe Mangioni,Ronaldo Menezes,Santo Fortunato. Complex Networks. Springer, 2009.

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41

Complex Networks. Pearson Higher Education & Professional Group, 2018.

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42

Complex Social Networks. New York: Cambridge University Press, 2007. http://dx.doi.org/10.1017/ccol0521857406.

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43

Vega-Redondo, Fernando. Complex Social Networks. Cambridge University Press, 2013.

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44

Kaminski, Bogumil, Pawel Prałat y Francois Theberge. Mining Complex Networks. Taylor & Francis Group, 2021.

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45

Kaminski, Bogumil, Pawel Prałat y Francois Theberge. Mining Complex Networks. Taylor & Francis Group, 2021.

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46

Kaminski, Bogumil, Pawel Pralat y Francois Theberge. Mining Complex Networks. CRC Press LLC, 2021.

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47

Mining Complex Networks. Taylor & Francis Group, 2021.

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48

Namatame, Akira y Takanori Komatsu. Modeling of Desirable Socioeconomic Networks. Editado por Shu-Heng Chen, Mak Kaboudan y Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.15.

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This chapter discusses the issues of designing desirable socioeconomic networks. Such networks permeate our lives. Evidence of this has generated increasing interest in dynamic processes in complex networks, especially the interplay between processes and the influences of network structure on performance and robustness. Performance optimization and robustness are important issues of socioeconomic networks. Diffusion is the process by which new products are invented and successfully introduced into a society (good diffusion) or infectious diseases spread (bad diffusion). Many studies shed light on how network topology interacts with the structure of social networked systems such as financial institutions to determine systemwide crises. In this context, entire classes of optimization problems range from maximizing the diffusion of innovations to minimizing risk distributions and cascade failures. The structure of interconnections influences network performance.
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49

Sheaff, Rod y Jill Schofield. Inter-Organizational Networks in Health Care. Editado por Ewan Ferlie, Kathleen Montgomery y Anne Reff Pedersen. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780198705109.013.29.

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Inter-organizational networks have proliferated in health systems, as has network research, but coherent explanations relating the varieties of health network to their respective structures, activities and outcomes remain lacking. Focusing on their core productive processes and their governance structures, this chapter contrasts care networks with program networks. It compares these concepts with findings from some primary research on NHS health networks during 2005–10, and notes some implications for network theory and research. NHS networks’ dense, flat structures reflect these networks’ dual function as both care and as program networks. These findings are relevant to the “integrated care” networks developing in many health systems. The development of these networks appears, partly, to be a workaround for the obstacles that market and quasi-market health systems place in the way of coordinating complex care across multiple separate providers.
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50

Cherifi, Hocine, Chantal Cherifi, Márton Karsai y Mirco Musolesi. Complex Networks & Their Applications VI: Proceedings of Complex Networks 2017. Springer, 2018.

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