报告题目：Structures and Dynamics of Complex Networks（复杂网络的结构和动态）
报告人： 高建喜 assistant professor，美国伦斯勒理工学院
邀请人： 宋燕 副教授
报告人介绍：Dr. Jianxi Gao is an assistant professor at the Department of Computer Science and Center for Network Science and Technology at Rensselaer Polytechnic Institute (RPI). Prior to joining the Department of Computer Science at RPI, he was a Research Assistant Professor at the Center for Complex Network Research at Northeastern University from 2012, working with Prof. Albert-László Barabási. Dr. Gao got his Ph. D. degree in the Department of Automation at Shanghai Jiao Tong University from 2008 to 2012. During his Ph.D. from 2009 to 2012 he visited Prof. H. Eugene. Stanley in Physics department at Boston University, as well as Prof. Shlomo Havlin in Physics department at Bar-IlanUniversity in 2012. His major contribution includes the theory for robustness of networks of networks and resilience of complex networks. Since 2010, he has published over 20 papers on journals, such as Nature, Nature Physics, Nature Communications, Proceedings of the National Academy of Sciences, Physical Review Letters and more, with over 2,000 citations on Google Scholar. I have been selected as the Editor board of Nature Scientific Reports, distinguished referee of EPL (2014-2016) and Elsevier (2016), and referee of Science, nature communications, PNAS, PRL, PRX and more. His publications were reported over 60 times by international public and professional media.
报告内容：This talk focuses on how to understand, predict, control, and ultimately survive real-world complex systems in an ever-changing world facing the global challenges of climate change, weather extremes, and other natural and human-induced disasters. I will present three recent works in the field of network science and complex systems: resilience, robustness, and control. (i) Resilience, a system's ability to adjust its activity to retain its basic functionality when errors and environmental changes occur, is a defining property of many complex systems. I will show a set of analytical tools with which to identify the natural control and state parameters of a multi-dimensional complex system, helping us derive an effective one-dimensional dynamics that accurately predict the system's resilience. The analytical results unveil the network characteristics that can enhance or diminish resilience, offering ways to prevent the collapse of ecological, biological or economic systems, and guiding the design of technological systems resilient to both internal failures and environmental changes. (ii) Increasing evidence shows that real-world systems interact with one another, and the real goal in network science shouldn't just understand individual networks, but deciphering the dynamical interactions in networks of networks (NONs). Malfunction of a few nodes in one network layer can cause cascading failures and catastrophic collapse of the entire system. I will show the general theoretical framework for analyzing the robustness of and cascading failures in NONs. The results of NONs have been surprisingly rich, and they differ from those of single networks that they present a new paradigm. (iii) Controlling complex networks is the ultimate goal of understanding the dynamics of them. I will present a k-walk theory and greedy algorithm for target control of complex networks.