© 2013

Complex Networks

  • Ronaldo Menezes
  • Alexandre Evsukoff
  • Marta C. González

Part of the Studies in Computational Intelligence book series (SCI, volume 424)

Table of contents

  1. Front Matter
    Pages 1-8
  2. Alireza Abbasi, Liaquat Hossain
    Pages 1-7
  3. Ming Liu, Elena Dubrova
    Pages 19-30
  4. Haoxiang Xia, Yanyan Du, Zhaoguo Xuan
    Pages 31-38
  5. Marcos A. C. Oliveira Junior, Carmelo J. A. Bastos Filho, Ronaldo Menezes
    Pages 39-47
  6. Amanda Leonel, Carlos H. C. Ribeiro, Matthias R. Brust
    Pages 49-56
  7. Anne-Laure Jousselme, Kevin Huggins, Nicolas Léchevin, Patrick Maupin, Dominic Larkin
    Pages 57-65
  8. Hugo S. Barbosa Filho, Fernando B. Lima Neto, Wilson Fusco
    Pages 67-74
  9. Vinícius da Fonseca Vieira, Alexandre Gonçalves Evsukoff
    Pages 75-86
  10. Massoud Seifi, Ivan Junier, Jean-Baptiste Rouquier, Svilen Iskrov, Jean-Loup Guillaume
    Pages 87-98
  11. Keziban Orman, Vincent Labatut, Hocine Cherifi
    Pages 99-110
  12. Priya Saha, Ronaldo Menezes
    Pages 131-139
  13. Robert Chen, Catherine Cramer, Pam DiBona, Russel Faux, Stephen Uzzo
    Pages 141-147
  14. Pramod Divakarmurthy, Ronaldo Menezes
    Pages 177-185

About this book


In the last decade we have seen the emergence of a new inter-disciplinary field focusing on the understanding of networks which are dynamic, large, open, and have a structure sometimes called random-biased. The field of Complex Networks is helping us better understand many complex phenomena such as the spread of  deseases, protein interactions, social relationships, to name but a few.

Studies in Complex Networks are gaining attention due to some major scientific breakthroughs proposed by network scientists helping us understand and model interactions contained in large datasets. In fact, if we could point to one event leading to the widespread use of complex network analysis is the availability of online databases. Theories of Random Graphs from Erdös and Rényi from the late 1950s led us to believe that most networks had random characteristics. The work on large online datasets told us otherwise. Starting with the work of Barabási and Albert as well as Watts and Strogatz in the late 1990s, we now know that most real networks are characterized by degree distributions that fit a power law (scale-free networks), and that they are highly clustered with small average path lengths between its nodes (small-world networks).

 It is therefore safe to state that the field of Complex Networks (sometimes called Network Sciences) is one of the most promising interdisciplinary disciplines of today. The sample of works in this book gives as a taste of what is in the horizon such controlling the dynamics of a network and in the network, using social interactions to improve urban planning, ranking in music and other art forms, and the understanding of knowledge transfer in influence networks.


Complex Networks Complexity Computational Intelligence

Editors and affiliations

  • Ronaldo Menezes
    • 1
  • Alexandre Evsukoff
    • 2
  • Marta C. González
    • 3
  1. 1.Dept. Computer SciencesFlorida Institute of TechnologyMelbourne, FLUSA
  2. 2.COPPE/Federal Univ. of Rio de JaneiroRio de Janeiro, RJBrazil
  3. 3.Massachusetts Institute of TechnologyCambridgeUSA

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