Fitness-Based Generative Models for Power-Law Networks

Khanh Nguyen (knguyen@cs.umb.edu) and Duc A. Tran (duc@cs.umb.edu)
Additional contact information
Khanh Nguyen: University of Massachusets
Duc A. Tran: University of Massachusets

Chapter Chapter 2 in Handbook of Optimization in Complex Networks, 2012, pp 39-53 from Springer

Abstract: 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.

Keywords: Real-world Complex Networks; Heavy-tailed Degree Distribution; Preferential Attachment; Sexual Contact Networks; Random Fitness (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (2)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-1-4614-0754-6_2

Ordering information: This item can be ordered from
http://www.springer.com/9781461407546

DOI: 10.1007/978-1-4614-0754-6_2

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla (sonal.shukla@springer.com) and Springer Nature Abstracting and Indexing (indexing@springernature.com).