IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-1-4614-0754-6_2.html
   My bibliography  Save this book chapter

Fitness-Based Generative Models for Power-Law Networks

In: Handbook of Optimization in Complex Networks

Author

Listed:
  • Khanh Nguyen

    (University of Massachusets)

  • Duc A. Tran

    (University of Massachusets)

Abstract

Many real-world complex networks exhibit a power-law degree distribution. A dominant concept traditionally believed to underlie the emergence of this phenomenon is the mechanism of preferential attachment which originally states that in a growing network a node with higher degree is more likely to be connected by joining nodes. However, a line of research towards a naturally comprehensible explanation for the formation of power-law networks has argued that degree is not the only key factor influencing the network growth. Instead, it is conjectured that each node has a “fitness” representing its propensity to attract links. The concept of fitness is more general than degree; the former may be some factor that is not degree, or may be degree in combination with other factors. This chapter presents a discussion of existing models for generating power-law networks, that belong to this approach.

Suggested Citation

  • Khanh Nguyen & Duc A. Tran, 2012. "Fitness-Based Generative Models for Power-Law Networks," Springer Optimization and Its Applications, in: My T. Thai & Panos M. Pardalos (ed.), Handbook of Optimization in Complex Networks, edition 1, chapter 0, pages 39-53, Springer.
    • Handle: RePEc:spr:spochp:978-1-4614-0754-6_2
    DOI: 10.1007/978-1-4614-0754-6_2
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mittal, Shravika & Chakraborty, Tanmoy & Pal, Siddharth, 2022. "Dynamics of node influence in network growth models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    2. Prasan Ratnayake & Sugandima Weragoda & Janaka Wansapura & Dharshana Kasthurirathna & Mahendra Piraveenan, 2021. "Quantifying the Robustness of Complex Networks with Heterogeneous Nodes," Mathematics, MDPI, vol. 9(21), pages 1-20, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:spochp:978-1-4614-0754-6_2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.