多层网络级联失效的预防和恢复策略概述

现实生活中,与国计民生密切相关的基础设施网络大多不是独立存在的,而是彼此之间相互联系或依赖的,于是用于研究这些系统的多层网络模型随之产生.多层网络中的节点在失效或者遭受攻击后会因"层内"和"层间"的相互作用而产生级联效应,从而使得失效能够在网络层内和层间反复传播并使得失效规模逐步放大.因此,多层网络比单个网络更加脆弱.多层网络级联失效产生的影响和损失往往是非常巨大的,所以对多层网络级联失效的预防和恢复的研究具有重大意义.就多层网络级联失效的预防而言,主要包含故障检测,保护重要节点,改变网络耦合机制和节点备份等策略.就多层网络发生级联失效后的恢复策略而言,主要包含共同边界节点恢复、空闲连边恢复、加边恢复、重要节点优先恢复、更改拓扑结构、局域攻击修复、自适应边修复等策略.     

Modeling the dynamics of firms' technological impact

Recent studies in complexity science have uncovered temporal regularities in the dynamics of impact along scientific and other creative careers, but they did not extend the obtained insights to firms. In this paper, we show that firms' technological impact patterns cannot be captured by the state-of-the-art dynamical models for the evolution of scientists' research impact, such as the Q model. Therefore, we propose a time-varying returns model which integrates the empirically-observed relation between patent order and technological impact into the Q model. The proposed model can reproduce the timing pattern of firms' highest-impact patents accurately. Our results shed light on modeling the differences behind the impact dynamics of researchers and firms.     

Influence fast or later: Two types of influencers in social networks

In real-world networks, there usually exist a small set of nodes that play an important role in the structure and function of networks. Those vital nodes can influence most of other nodes in the network via a spreading process. While most of the existing works focused on vital nodes that can maximize the spreading size in the final stage, which we call final influencers, recent work proposed the idea of fast influencers, which emphasizes nodes' spreading capacity at the early stage. Despite the recent surge of efforts in identifying these two types of influencers in networks, there remained limited research on untangling the differences between the fast influencers and final influencers. In this paper, we firstly distinguish the two types of influencers: fast-only influencers and final-only influencers. The former is defined as individuals who can achieve a high spreading effect at the early stage but lose their superiority in the final stage, and the latter are those individuals that fail to exhibit a prominent spreading performance at the early stage but influence a large fraction of nodes at the final stage. Further experiments are based on eight empirical datasets, and we reveal the key differences between the two types of influencers concerning their spreading capacity and the local structures. We also analyze how network degree assortativity influences the fraction of the proposed two types of influencers. The results demonstrate that with the increase of degree assortativity, the fraction of the fast-only influencers decreases, which indicates that more fast influencers tend to keep their superiority at the final stage. Our study provides insights into the differences and evolution of different types of influencers and has important implications for various empirical applications, such as advertisement marketing and epidemic suppressing.