A weighted network model based on node fitness dynamic evolution

Many complex networks in practice can be described by weighted networks. Currently, most existing weighted network models only consider the node strength in evolving conditions, but neglect the influence of node attraction on network evolution. In this paper, we propose an accurate and practical weighted evolving network model based on node fitness dynamic evolution, which takes both node strength and node attraction into consideration. Our theoretical analysis and numerical simulations have demonstrated the scale-free property of the network model, which has been widely observed in many real-world networks. Additionally, the phenomenon that very few nodes possess greater fitness is observed via numerical simulations of our network model, which can be referred to as the fitness property of network. Our network model's dual assessment of node strength and node attraction leads to fewer node clustering and stronger robustness of the whole network than other existing network growth models.