title: Toward link predictability of complex networks.
abstract: The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners.
authors: Alibaba Research Center for Complexity Sciences, Alibaba Business College, Hangzhou Normal University, Hangzhou 311121, China; hes@bu.edu linyuan.lv@hznu.edu.cn yi-cheng.zhang@unifr.ch.
authors: Linyuan
authors: L
authors: Lü
authors: Alibaba Research Center for Complexity Sciences, Alibaba Business College, Hangzhou Normal University, Hangzhou 311121, China; CompleX Lab, Web Sciences Center and.
authors: Liming
authors: L
authors: Pan
authors: CompleX Lab, Web Sciences Center and Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
authors: Tao
authors: T
authors: Zhou
authors: Alibaba Research Center for Complexity Sciences, Alibaba Business College, Hangzhou Normal University, Hangzhou 311121, China; Department of Physics, University of Fribourg, Fribourg CH-1700, Switzerland; and hes@bu.edu linyuan.lv@hznu.edu.cn yi-cheng.zhang@unifr.ch.
authors: Yi-Cheng
authors: YC
authors: Zhang
authors: Alibaba Research Center for Complexity Sciences, Alibaba Business College, Hangzhou Normal University, Hangzhou 311121, China; Department of Physics and Center for Polymer Studies, Boston University, Boston, MA 02215 hes@bu.edu linyuan.lv@hznu.edu.cn yi-cheng.zhang@unifr.ch.
authors: H Eugene
authors: HE
authors: Stanley
keywords: complex networks; link prediction; predictability; structural perturbation