Fitness-Based Generative Models for Power-Law Networks

Khanh Nguyen () and Duc A. Tran ()
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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
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DOI: 10.1007/978-1-4614-0754-6_2

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