Network robustness analysis based on current road incident data

2015 ◽  
pp. 1763-1768
Author(s):  
R Vodák ◽  
R Andrášik ◽  
M Bíl ◽  
J Sedoník
Keyword(s):  
Robustness Analysis ◽  
Network Robustness ◽  
Incident Data

scholarly journals Interdependency and Vulnerability of Multipartite Networks under Target Node Attacks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Qing Cai ◽  
Mahardhika Pratama ◽  
Sameer Alam
Keyword(s):  
Complex Networks ◽  
Robustness Analysis ◽  
Network Models ◽  
Continuous Phase ◽  
Structure Design ◽  
Network Robustness ◽  
Multilayer Networks ◽  
Component Theory ◽  
Centrality Metrics ◽  
Entire Network

Complex networks in reality may suffer from target attacks which can trigger the breakdown of the entire network. It is therefore pivotal to evaluate the extent to which a network could withstand perturbations. The research on network robustness has proven as a potent instrument towards that purpose. The last two decades have witnessed the enthusiasm on the studies of network robustness. However, existing studies on network robustness mainly focus on multilayer networks while little attention is paid to multipartite networks which are an indispensable part of complex networks. In this study, we investigate the robustness of multipartite networks under intentional node attacks. We develop two network models based on the largest connected component theory to depict the cascading failures on multipartite networks under target attacks. We then investigate the robustness of computer-generated multipartite networks with respect to eight node centrality metrics. We discover that the robustness of multipartite networks could display either discontinuous or continuous phase transitions. Interestingly, we discover that larger number of partite sets of a multipartite network could increase its robustness which is opposite to the phenomenon observed on multilayer networks. Our findings shed new lights on the robust structure design of complex systems. We finally present useful discussions on the applications of existing percolation theories that are well studied for network robustness analysis to multipartite networks. We show that existing percolation theories are not amenable to multipartite networks. Percolation on multipartite networks still deserves in-depth efforts.

scholarly journals A multiple perspective method for urban subway network robustness analysis

AIP Advances ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. 075219 ◽  
Author(s):  
Shuliang Wang ◽  
Sen Nie ◽  
Longfeng Zhao ◽  
H. Eugene Stanley
Keyword(s):  
Robustness Analysis ◽  
Network Robustness ◽  
Multiple Perspective

scholarly journals Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Xiaole Wan ◽  
Zhen Zhang ◽  
Chi Zhang ◽  
Qingchun Meng
Keyword(s):  
Stock Market ◽  
Complex Networks ◽  
Robustness Analysis ◽  
Risk Identification ◽  
Network Robustness ◽  
Degree Centrality ◽  
Market Risks ◽  
Stationary Test ◽  
Systematic Risks ◽  
Quantitative Indexes

The Chinese stock 300 index (CSI 300) is widely accepted as an overall reflection of the general movements and trends of the Chinese A-share markets. Among the methodologies used in stock market research, the complex network as the extension of graph theory presents an edged tool for analyzing internal structure and dynamic involutions. So, the stock data of the CSI 300 were chosen and divided into two time series, prepared for analysis via network theory. After stationary test and coefficients calculated for daily amplitudes of stock, two “year-round” complex networks were constructed, respectively. Furthermore, the network indexes, including out degree centrality, in degree centrality, and betweenness centrality, were analyzed by taking negative correlations among stocks into account. The first 20 stocks in the market networks, termed “major players,” “gatekeeper,” and “vulnerable players,” were explored. On this basis, temporal networks were constructed and the algorithm to test robustness was designed. In addition, quantitative indexes of robustness and evaluation standards of network robustness were introduced and the systematic risks of the stock market were analyzed. This paper enriches the theory on temporal network robustness and provides an effective tool to prevent systematic stock market risks.

scholarly journals Percolation Theories for Multipartite Networked Systems under Random Failures

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Qing Cai ◽  
Sameer Alam ◽  
Mahardhika Pratama ◽  
Zhen Wang
Keyword(s):  
Complex Systems ◽  
Robustness Analysis ◽  
Network Models ◽  
Networked Systems ◽  
Network Robustness ◽  
Cascading Failures ◽  
Connected Component ◽  
System Components ◽  
Random Node ◽  
Random Failures

Real-world complex systems inevitably suffer from perturbations. When some system components break down and trigger cascading failures on a system, the system will be out of control. In order to assess the tolerance of complex systems to perturbations, an effective way is to model a system as a network composed of nodes and edges and then carry out network robustness analysis. Percolation theories have proven as one of the most effective ways for assessing the robustness of complex systems. However, existing percolation theories are mainly for multilayer or interdependent networked systems, while little attention is paid to complex systems that are modeled as multipartite networks. This paper fills this void by establishing the percolation theories for multipartite networked systems under random failures. To achieve this goal, this paper first establishes two network models to describe how cascading failures propagate on multipartite networks subject to random node failures. Afterward, this paper adopts the largest connected component concept to quantify the networks’ robustness. Finally, this paper develops the corresponding percolation theories based on the developed network models. Simulations on computer-generated multipartite networks demonstrate that the proposed percolation theories coincide quite well with the simulations.

scholarly journals Network Robustness Analysis Based on Maximum Flow

2021 ◽  
Vol 9 ◽  
Author(s):  
Meng Cai ◽  
Jiaqi Liu ◽  
Ying Cui
Keyword(s):  
Structural Integrity ◽  
Random Network ◽  
Robustness Analysis ◽  
Maximum Flow ◽  
Indicator System ◽  
Average Degree ◽  
Network Robustness ◽  
Damage Rate ◽  
Flow Capacity ◽  
Critical Damage

Network robustness is the ability of a network to maintain a certain level of structural integrity and its original functions after being attacked, and it is the key to whether the damaged network can continue to operate normally. We define two types of robustness evaluation indicators based on network maximum flow: flow capacity robustness, which assesses the ability of the network to resist attack, and flow recovery robustness, which assesses the ability to rebuild the network after an attack on the network. To verify the effectiveness of the robustness indicators proposed in this study, we simulate four typical networks and analyze their robustness, and the results show that a high-density random network is stronger than a low-density network in terms of connectivity and resilience; the growth rate parameter of scale-free network does not have a significant impact on robustness changes in most cases; the greater the average degree of a regular network, the greater the robustness; the robustness of small-world network increases with the increase in the average degree. In addition, there is a critical damage rate (when the node damage rate is less than this critical value, the damaged nodes and edges can almost be completely recovered) when examining flow recovery robustness, and the critical damage rate is around 20%. Flow capacity robustness and flow recovery robustness enrich the network structure indicator system and more comprehensively describe the structural stability of real networks.

Network Robustness Analysis for IoT Networks using Regular Graphs

2021 ◽  
pp. 1-1
Author(s):  
Sateeshkrishna Dhuli ◽  
Said Kouachi ◽  
Anamika Chhabra ◽  
Yatindra Nath Singh
Keyword(s):  
Robustness Analysis ◽  
Network Robustness ◽  
Regular Graphs
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