Fitness-Based Generative Models for Power-Law Networks

Part of the Springer Optimization and Its Applications book series (SOIA, volume 57)


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.



The authors would like to thank UMass Boston colleagues, Shilpa Ghadge, Bala Sundaram, and Timothy Killingback, for valuable discussions regarding the lognormal fitness model presented in this chapter.


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© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of MassachusetsBostonUSA

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