title: Measuring multiple evolution mechanisms of complex networks.
abstract: Numerous concise models such as preferential attachment have been put forward to reveal the evolution mechanisms of real-world networks, which show that real-world networks are usually jointly driven by a hybrid mechanism of multiplex features instead of a single pure mechanism. To get an accurate simulation for real networks, some researchers proposed a few hybrid models by mixing multiple evolution mechanisms. Nevertheless, how a hybrid mechanism of multiplex features jointly influence the network evolution is not very clear. In this study, we introduce two methods (link prediction and likelihood analysis) to measure multiple evolution mechanisms of complex networks. Through tremendous experiments on artificial networks, which can be controlled to follow multiple mechanisms with different weights, we find the method based on likelihood analysis performs much better and gives very accurate estimations. At last, we apply this method to some real-world networks which are from different domains (including technology networks and social networks) and different countries (e.g., USA and China), to see how popularity and clustering co-evolve. We find most of them are affected by both popularity and clustering, but with quite different weights.
authors.affiliation: 1] Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China [2] Center for Polymer Studies, Department of Physics, Boston University, Boston 02215, United States of America [3] Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
authors.forename: Qian-Ming
authors.initials: QM
authors.lastname: Zhang
authors.affiliation: College of Information and Communication Engineering, Dalian Nationalities University, Dalian 116600, People's Republic of China.
authors.forename: Xiao-Ke
authors.initials: XK
authors.lastname: Xu
authors.affiliation: 1] Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China [2] Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
authors.forename: Yu-Xiao
authors.initials: YX
authors.lastname: Zhu
authors.affiliation: 1] Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China [2] Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
authors.forename: Tao
authors.initials: T
authors.lastname: Zhou
mesh_terms: D006801:Humans; D008962:Models, Theoretical; D012944:Social Support