题目：Towards Link Predictability of Complex Networks 复杂网络的链路可预测性
摘要：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 network links. In order 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 measure of network predictability, and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and universality. 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 IT practitioners.
主讲人介绍：吕琳媛，博士，杭州师范大学阿里巴巴商学院教授（27岁正教授），阿里巴巴中欧联合实验室副主任兼执行主任，链路预测实验室负责人。专注于复杂网络信息挖掘方向的研究，利用统计物理和复杂网络的理论和方法来解决信息领域中的若干重要问题，包括海量信息的导航、挖掘、推荐和预测。至今已发表论文47篇，其中两篇ESI全球1%高引用论文。论文SCI总引用600余次，其中他引500余次，谷歌学术总引用1700余次，引用超过100次的论文三篇，单篇论文最高引用350次。发表期刊包括国际顶尖学术期刊《Physics Reports》（IF=25.010），Nature子刊《Scientific Reports》（IF=5.078），《PLoS One》，《New J Phys》，《Phys Rev E》，《EPL》，以及国内权威期刊《中国科学》和《科学通报》等。2012年获杭州师范大学优秀科研工作者；2013年荣获杭州市中青年学术带头人。2014年获中国网络科学论坛青年希望奖。
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