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有向网络网络中的多重相变。

Multiple phase transitions in networks of directed networks.

机构信息

Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.

Department of Physics, Center for Polymer Studies, Boston University, Boston, Massachusetts 02215, USA.

出版信息

Phys Rev E. 2019 Jan;99(1-1):012312. doi: 10.1103/PhysRevE.99.012312.

Abstract

The robustness in real-world complex systems with dependency connectivities differs from that in isolated networks. Although most complex network research has focused on interdependent undirected systems, many real-world networks-such as gene regulatory networks and traffic networks-are directed. We thus develop an analytical framework for examining the robustness of networks made up of directed networks of differing topologies. We use it to predict the phase transitions that occur during node failures and to generate the phase diagrams of a number of different systems, including treelike and random regular (RR) networks of directed Erdős-Rényi (ER) networks and scale-free networks. We find that the the phase transition and phase diagram of networks of directed networks differ from those of networks of undirected networks. For example, the RR networks of directed ER networks show a hybrid phase transition that does not occur in networks of undirected ER networks. In addition, system robustness is affected by network topology in networks of directed networks. As coupling strength q increases, treelike networks of directed ER networks change from a second-order phase transition to a first-order phase transition, and RR networks of directed ER networks change from a second-order phase transition to a hybrid phase transition, then to a first-order phase transition, and finally to a region of collapse. We also find that heterogenous network systems are more robust than homogeneous network systems. We note that there are multiple phase transitions and triple points in the phase diagram of RR networks of directed networks and this helps us understand how to increase network robustness when designing interdependent infrastructure systems.

摘要

具有依赖关系的复杂实际系统的稳健性与孤立网络不同。尽管大多数复杂网络研究都集中在相互依存的无向系统上,但许多实际网络,如基因调控网络和交通网络,都是有向的。因此,我们开发了一个分析框架来研究由不同拓扑结构的有向网络组成的网络的稳健性。我们使用它来预测节点故障期间发生的相变,并生成许多不同系统的相图,包括有向 Erdos-Rényi (ER) 网络的树状和随机规则 (RR) 网络以及无标度网络。我们发现有向网络的网络的相变和相图与无向网络的网络不同。例如,有向 ER 网络的 RR 网络显示出混合相变,而无向 ER 网络的网络中则不会发生这种相变。此外,在有向网络中,网络拓扑会影响系统的稳健性。随着耦合强度 q 的增加,有向 ER 网络的树状网络从二阶相变变为一阶相变,而有向 ER 网络的 RR 网络从二阶相变变为混合相变,然后变为一阶相变,最后变为崩溃区域。我们还发现,异质网络系统比同质网络系统更稳健。我们注意到,有向网络的 RR 网络的相图中有多个相变点和三重点,这有助于我们了解在设计相互依存的基础设施系统时如何提高网络的稳健性。

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