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.
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Acknowledgements
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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Nguyen, K., Tran, D.A. (2012). Fitness-Based Generative Models for Power-Law Networks. In: Thai, M., Pardalos, P. (eds) Handbook of Optimization in Complex Networks. Springer Optimization and Its Applications(), vol 57. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-0754-6_2
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