title: Spontaneous scale-free structure in adaptive networks with synchronously dynamical linking.
abstract: Inspired by the anti-Hebbian learning rule in neural systems, we study how the feedback from dynamical synchronization shapes network structure by adding new links. Through extensive numerical simulations, we find that an adaptive network spontaneously forms scale-free structure, as confirmed in many real systems. Moreover, the adaptive process produces two nontrivial power-law behaviors of deviation strength from mean activity of the network and negative degree correlation, which exists widely in technological and biological networks. Importantly, these scalings are robust to variation of the adaptive network parameters, which may have meaningful implications in the scale-free formation and manipulation of dynamical networks. Our study thus suggests an alternative adaptive mechanism for the formation of scale-free structure with negative degree correlation, which means that nodes of high degree tend to connect, on average, with others of low degree and vice versa. The relevance of the results to structure formation and dynamical property in neural networks is briefly discussed as well.
authors.affiliation: College of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China and Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.
authors.forename: Wu-Jie
authors.initials: WJ
authors.lastname: Yuan
authors.forename: Jian-Fang
authors.initials: JF
authors.lastname: Zhou
authors.forename: Qun
authors.initials: Q
authors.lastname: Li
authors.forename: De-Bao
authors.initials: DB
authors.lastname: Chen
authors.forename: Zhen
authors.initials: Z
authors.lastname: Wang
mesh_terms: D005246:Feedback; D008962:Models, Theoretical