Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31499
Title: Fitness-driven deactivation in network evolution
Authors: Xu, XJ
Peng, XL
Small, M
Fu, XC
Keywords: Network dynamics
Networks
Random graphs
Issue Date: 2010
Publisher: IOP Publishing Ltd
Source: Journal of statistical mechanics: theory and experiment, 2010, v. 2010, no. 12, p12020 How to cite?
Journal: Journal of Statistical Mechanics: Theory and Experiment 
Abstract: Individual nodes in evolving real-world networks typically experience growth and decay-that is, the popularity and influence of individuals peaks and then fades. In this paper, we study this phenomenon via an intrinsic nodal fitness function and an intuitive ageing mechanism. Each node of the network is endowed with a fitness which represents its activity. All the nodes have two discrete stages: active and inactive. The evolution of the network combines the addition of new active nodes randomly connected to existing active ones and the deactivation of old active nodes with a possibility inversely proportional to their fitnesses. We obtain a structured exponential network when the fitness distribution of the individuals is homogeneous and a structured scale-free network with heterogeneous fitness distributions. Furthermore, we recover two universal scaling laws of the clustering coefficient for both cases, C(k) ∼ k -1 and C ∼ n -1, where k and n refer to the node degree and the number of active individuals, respectively. These results offer a new simple description of the growth and ageing of networks where intrinsic features of individual nodes drive their popularity, and hence degree.
URI: http://hdl.handle.net/10397/31499
DOI: 10.1088/1742-5468/2010/12/P12020
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