[Postgraduates] Cascading failures and recovery in networks of networks

Time: 8:30-11:30, June 6  
Place:No. 8 Reporting Hall, Conference Cente of New Main Building 新主楼会议中心 第八会议室
Lecture 1
Topic Cascading failures and recovery in networks of networks
Lecturer: Shlomo Havlin  
Abstract:Network science have been focused on the properties of a single isolated network that does not interact or depends on other networks. In reality, many real-networks, such as power grids, transportation and communication infrastructures interact and depend on other networks. I will present a framework for studying the vulnerability and the recovery of networks of interdependent networks. In interdependent networks, when nodes in one network fail, they cause dependent nodes in other networks to also fail. This is also the case when some nodes like certain locations play a role in two networks --multiplex. This may happen recursively and can lead to a cascade of failures and to a sudden fragmentation of the system. I will present analytical solutions for the critical threshold and the giant component of a network of n interdependent networks. I will show, that the general theory has many novel features that are not present in the classical network theory. When recovery of components is possible global spontaneous recovery of the networks and hysteresis phenomena occur and the theory suggests an optimal repairing strategy of system of systems. I will also show that interdependent networks embedded in space are significantly more vulnerable compared to non embedded networks. In particular, small localized attacks may lead to cascading failures and catastrophic consequences. Thus, analyzing data of real network of networks is highly required to understand the system vulnerability.
Lecture 2
TopicWhich publication is your representative work?
Lecturer: DI Zengru 
Abstract:As much effort has been made to accelerate the publication of research results, nowadays the number of papers per scientist is much larger than before. In this context, how to identify the representative work for individual researcher is an important yet uneasy problem. Addressing it will help policy makers better evaluate the achievement and potential of researchers. So far, the representative work of a researcher is usually selected as his/her most highly cited paper or the paper published in top journals. Here, we consider the representative work of a scientist as an important paper in his/her area of expertise. Accordingly, we propose a self-avoiding preferential diffusion process to generate personalized ranking of papers for each scientist and identify their representative works. The citation data from American Physical Society (APS) is used to validate our method. We find that the self-avoiding preferential diffusion method can rank the Nobel prize winning paper in each Nobel laureate’s personal ranking list higher than the citation count and PageRank methods, indicating the effectiveness of our method. Moreover, the robustness analysis shows that our method can highly rank the representative papers of scientists even partial citation data are available or spurious behaviors exist. The method is finally applied to revealing the research patterns (i.e. consistency-oriented or diversity-oriented) of different scientists, institutes and countries.

Lecture 3
TopicCycles, feedback sets, and network optimal attack
Lecturer: ZHOU Haijun 
Abstract:One of the basic structural reasons that complex networks are "complex" is that they contain an abundant number of (long and short) cycles. Cycles lead to feedbacks and complex dynamical processes. The task of identifying the nodes  most important for feedback interactions is closely related to the concept of minimum feedback vertex set (FVS). In this presentation I introduce a spin-glass approach to the minimum FVS problem and offer a highly efficient algorithm for solving it. As an important application, I show that this algorithm also solves the optimal network attack problem perfectly. Our theory and algorithm shall be useful for understanding and improving network resilience.