Influence of Clustering on Network Robustness Against Epidemic Propagation

Network robustness improvement via long-range links

Computational Social Networks ◽

10.1186/s40649-019-0073-2 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 1

Author(s):

Vincenza Carchiolo ◽

Marco Grassia ◽

Alessandro Longheu ◽

Michele Malgeri ◽

Giuseppe Mangioni

Keyword(s):

Complex Networks ◽

Long Range ◽

Real World ◽

Network Robustness ◽

Scale Free ◽

Area Of Interest ◽

Before And After ◽

Significant Area ◽

Better Than

AbstractMany systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.

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Quantifying Road-Network Robustness toward Flood-Resilient Transportation Systems

Sustainability ◽

10.3390/su13063172 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3172

Author(s):

Suchat Tachaudomdach ◽

Auttawit Upayokin ◽

Nopadon Kronprasert ◽

Kriangkrai Arunotayanun

Keyword(s):

Road Network ◽

Critical Infrastructure ◽

Policy Issue ◽

Analytical Framework ◽

Transportation Systems ◽

Network Robustness ◽

System Response ◽

Chiang Mai ◽

Protection Measures ◽

Response And Recovery

Amidst sudden and unprecedented increases in the severity and frequency of climate-change-induced natural disasters, building critical infrastructure resilience has become a prominent policy issue globally for reducing disaster risks. Sustainable measures and procedures to strengthen preparedness, response, and recovery of infrastructures are urgently needed, but the standard for measuring such resilient elements has yet to be consensually developed. This study was undertaken with an aim to quantitatively measure transportation infrastructure robustness, a proactive dimension of resilience capacities and capabilities to withstand disasters; in this case, floods. A four-stage analytical framework was empirically implemented: 1) specifying the system and disturbance (i.e., road network and flood risks in Chiang Mai, Thailand), 2) illustrating the system response using the damaged area as a function of floodwater levels and protection measures, 3) determining recovery thresholds based on land use and system functionality, and 4) quantifying robustness through the application of edge- and node-betweenness centrality models. Various quantifiable indicators of transportation robustness can be revealed; not only flood-damaged areas commonly considered in flood-risk management and spatial planning, but also the numbers of affected traffic links, nodes, and cars are highly valuable for transportation planning in achieving sustainable flood-resilient transportation systems.

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A landscape experiment of spatial network robustness and space‐use reorganization following habitat fragmentation

Functional Ecology ◽

10.1111/1365-2435.13380 ◽

2019 ◽

Vol 33 (9) ◽

pp. 1663-1673 ◽

Cited By ~ 3

Author(s):

Marie‐Caroline Prima ◽

Thierry Duchesne ◽

André Fortin ◽

Louis‐Paul Rivest ◽

Pierre Drapeau ◽

...

Keyword(s):

Habitat Fragmentation ◽

Space Use ◽

Network Robustness ◽

Spatial Network

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Identification and Quantification of Node Criticality through EWM–TOPSIS: A Study of Hong Kong’s MTR System

Urban Rail Transit ◽

10.1007/s40864-021-00155-6 ◽

2021 ◽

Author(s):

Yifan Zhang ◽

S. Thomas Ng

Keyword(s):

Hong Kong ◽

Public Transportation ◽

Early Stage ◽

Network Robustness ◽

Global Network ◽

Practical Significance ◽

Topsis Method ◽

Urban Resilience ◽

Network Efficiency ◽

Entropy Weight

AbstractPublic transport networks (PTNs) are critical in populated and rapidly densifying cities such as Hong Kong, Beijing, Shanghai, Mumbai, and Tokyo. Public transportation plays an indispensable role in urban resilience with an integrated, complex, and dynamically changeable network structure. Consequently, identifying and quantifying node criticality in complex PTNs is of great practical significance to improve network robustness from damage. Despite the proposition of various node criticality criteria to address this problem, few succeeded in more comprehensive aspects. Therefore, this paper presents an efficient and thorough ranking method, that is, entropy weight method (EWM)–technology for order preference by similarity to an ideal solution (TOPSIS), named EWM–TOPSIS, to evaluate node criticality by taking into account various node features in complex networks. Then we demonstrate it on the Mass Transit Railway (MTR) in Hong Kong by removing and recovering the top k critical nodes in descending order to compare the effectiveness of degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), and the proposed EWM–TOPSIS method. Four evaluation indicators, that is, the frequency of nodes with the same ranking (F), the global network efficiency (E), the size of the largest connected component (LCC), and the average path length (APL), are computed to compare the performance of the four methods and measure network robustness under different designed attack and recovery strategies. The results demonstrate that the EWM–TOPSIS method has more obvious advantages than the others, especially in the early stage.

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