题目: The simplified self-consistent probabilities method for percolation and its application to interdependent networks
演讲人:Feng Ling
Speaker: Dr. Feng Ling, Complex System Group, IHPC, A*STAR Singapore
新加坡科技研究局，高性能计算研究所
时间: 11月13号(星期五)上午10:10-11:00
地点:管理学院4楼第二会议室(小会议室)
摘要: Interdependent networks are ubiquitous in our society, ranging from infrastructure to economics, and the study of their cascading behaviors using percolation theory has attracted much attention in the recent years. To analyze the percolation phenomena of these systems, different mathematical frameworks have been proposed including generating functions, eigenvalues among some others. These different frameworks approach the phase transition behaviors from different angles, and have been very successful in shaping the different quantities of interest including critical threshold, size of the giant component, order of phase transition and the dynamics of cascading. These methods also vary in their mathematical complexity in dealing with interdependent networks that have additional complexity in terms of the correlation among different layers of networks or links. In this work, we review a particular approach of simple self-consistent probability equations, and illustrate that it can greatly simplify the mathematical analysis for systems ranging from single layer network to various different interdependent networks. We give an overview on the detailed framework to study the nature of the critical phase transition, value of the critical threshold and size of the giant component for these different systems.
演讲人介绍
Dr. Feng Ling is a scientist working at the Complex Systems group in Institute of High Performance Computing (IHPC), A*STAR. His received his PhD in Econophysics from National University of Singapore in 2013. During 2011-2013, he worked in Prof Eugene Stanley’s econophysics group at Boston University on complex networks and agent-based models in economics. His research interests mainly covers percolation and diffusion phenomenon in complex networks, and social media data analytics.
