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连接架构:网络脆弱性、复杂性和弹性的关键

The Architecture of Connectivity: A Key to Network Vulnerability, Complexity and Resilience.

作者信息

Reggiani Aura

机构信息

Department of Economics, University of Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy.

出版信息

Netw Spat Econ. 2022;22(3):415-437. doi: 10.1007/s11067-022-09563-y. Epub 2022 May 17.

DOI:10.1007/s11067-022-09563-y
PMID:35600162
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9111940/
Abstract

This paper highlights the relevance of connectivity and its architecture as a general conceptual framework which underlies and integrates the concepts of network vulnerability, complexity, and resilience. In particular, it will be pointed out that connectivity architecture can be considered an explicit key element for network vulnerability and shock propagation. While the relevance of the various connectivity configurations is not clearly emphasised in the dynamic complexity models of the space-economy, it appears to play a primary role in network analysis. In this regard, the emerging recognition of connectivity architecture in relation to hubs ‒ and hierarchies of hubs ‒ in a complex network will help the enhancement of network resilience. The paper develops as follows. First, the notion of network vulnerability, which refers not only to the phenomenon of shocks, but also to the propagation of shocks in a network, will be examined. Here it appears that modelling vulnerability and shock propagation, also jointly with cascading disaster models, is strongly based on connectivity issues. The question is: How can conventional (complex) system dynamic modelling, as well as network modelling, take into account these shocks and connectivity dynamics from the methodological viewpoint? A review in this respect shows how connectivity is a 'hidden' element in these complexity models, for example, in chaos or (dynamic) competition models, where interaction parameter values might lead to vulnerable domains and chaotic behaviour. On the contrary, connectivity and its various topologies have a distinct, primary role in network analysis. The issue of network resilience appears therefore to be the 'response' to vulnerability and chaos, calling for robustness and stability of the network in the presence of shocks and disruptions. Resilience analysis refers to the speed at which a network returns to its equilibrium after a shock, as well as to the perturbations/shocks that can be absorbed before the network is induced into some other equilibrium (adaptivity). Connectivity is relevant here, but not often considered in spatial economics. In order to reach a unified methodological framework, attention will finally be paid to a complementary analysis of the (dynamic) concepts of vulnerability and resilience. In this light, chaos models/properties might be seen in a positive perspective, since small changes can lead to uncertain and unstable effects, but also, thanks to connectivity, to new equilibria which are not necessarily negative. Thus, the architecture of connectivity, in its interdisciplinary insights, can be considered as a fundamental (and analytical) approach for identifying vulnerability and resilience patterns in complex networks.

摘要

本文强调了连通性及其架构作为一个通用概念框架的相关性,该框架构成并整合了网络脆弱性、复杂性和恢复力的概念。特别要指出的是,连通性架构可被视为网络脆弱性和冲击传播的一个明确关键要素。虽然在空间经济的动态复杂性模型中,各种连通性配置的相关性并未得到明确强调,但它在网络分析中似乎起着主要作用。在这方面,在复杂网络中对与枢纽以及枢纽层级相关的连通性架构的新认识,将有助于增强网络恢复力。本文内容如下。首先,将探讨网络脆弱性的概念,它不仅指冲击现象,还指冲击在网络中的传播。在此可以看出,对脆弱性和冲击传播进行建模,以及与级联灾害模型联合建模,都强烈基于连通性问题。问题是:从方法论角度来看,传统的(复杂)系统动态建模以及网络建模如何考虑这些冲击和连通性动态?这方面的一项综述表明,连通性在这些复杂性模型中是一个“隐藏”要素,例如在混沌或(动态)竞争模型中,相互作用参数值可能导致脆弱域和混沌行为。相反,连通性及其各种拓扑结构在网络分析中具有独特的主要作用。因此,网络恢复力问题似乎是对脆弱性和混沌的“响应”,要求网络在存在冲击和干扰时具备稳健性和稳定性。恢复力分析涉及网络在冲击后恢复到平衡的速度,以及在网络被诱导进入其他平衡(适应性)之前能够吸收的扰动/冲击。连通性在此相关,但在空间经济学中并不常被考虑。为了达成一个统一的方法论框架,最后将关注对脆弱性和恢复力的(动态)概念进行补充分析。据此,混沌模型/属性可能会从积极的角度来看待,因为小的变化可能导致不确定和不稳定的影响,但同时,由于连通性,也可能导致不一定是负面的新平衡。因此,连通性架构凭借其跨学科的见解,可被视为识别复杂网络中脆弱性和恢复力模式的一种基本(且分析性的)方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0a/9111940/e01349e15815/11067_2022_9563_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0a/9111940/512cf24c98e8/11067_2022_9563_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0a/9111940/e01349e15815/11067_2022_9563_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0a/9111940/512cf24c98e8/11067_2022_9563_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db0a/9111940/e01349e15815/11067_2022_9563_Fig2_HTML.jpg

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