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 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 Greedy control of cascading failures in interdependent networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Malgorzata Turalska ◽  
Ananthram Swami
Keyword(s):  
Optimal Control ◽  
Complex Systems ◽  
Control Strategy ◽  
Cascading Failures ◽  
Optimal Control Strategy ◽  
Supercritical Regime ◽  
Interdependent Networks ◽  
Malicious Activity ◽  
Cross System ◽  
Made In

AbstractComplex systems are challenging to control because the system responds to the controller in a nonlinear fashion, often incorporating feedback mechanisms. Interdependence of systems poses additional difficulties, as cross-system connections enable malicious activity to spread between layers, increasing systemic risk. In this paper we explore the conditions for an optimal control of cascading failures in a system of interdependent networks. Specifically, we study the Bak–Tang–Wiesenfeld sandpile model incorporating a control mechanism, which affects the frequency of cascades occurring in individual layers. This modification allows us to explore sandpile-like dynamics near the critical state, with supercritical region corresponding to infrequent large cascades and subcritical zone being characterized by frequent small avalanches. Topological coupling between networks introduces dependence of control settings adopted in respective layers, causing the control strategy of a given layer to be influenced by choices made in other connected networks. We find that the optimal control strategy for a layer operating in a supercritical regime is to be coupled to a layer operating in a subcritical zone, since such condition corresponds to reduced probability of inflicted avalanches. However this condition describes a parasitic relation, in which only one layer benefits. Second optimal configuration is a mutualistic one, where both layers adopt the same control strategy. Our results provide valuable insights into dynamics of cascading failures and and its control in interdependent complex systems.

Cascading failures of interdependent modular small-world networks

2016 ◽  
Vol 30 (18) ◽  
pp. 1650174 ◽  
Author(s):  
Guowei Zhu ◽  
Xianpei Wang ◽  
Meng Tian ◽  
Dangdang Dai ◽  
Jiachuan Long ◽  
...  
Keyword(s):  
Broad Class ◽  
Small World ◽  
Cascading Failure ◽  
Cascading Failures ◽  
Small World Networks ◽  
Connected Component ◽  
Random Coupling ◽  
Global And Local ◽  
The Relationship ◽  
Local Perspectives

Much empirical evidence shows that many real-world networks fall into the broad class of small-world networks and have a modular structure. The modularity has been revealed to have an important effect on cascading failure in isolated networks. However, the corresponding results for interdependent modular small-world networks remain missing. In this paper, we investigate the relationship between cascading failures and the intra-modular rewiring probabilities and inter-modular connections under different coupling preferences, i.e. random coupling with modules (RCWM), assortative coupling in modules (ACIM) and assortative coupling with modules (ACWM). The size of the largest connected component is used to evaluate the robustness from global and local perspectives. Numerical results indicate that increasing intra-modular rewiring probabilities and inter-modular connections can improve the robustness of interdependent modular small-world networks under intra-attacks and inter-attacks. Meanwhile, experiments on three coupling strategies demonstrate that ACIM has a better effect on preventing the cascading failures compared with RCWM and ACWM. These results can be helpful to allocate and optimize the topological structure of interdependent modular small-world networks to improve the robustness of such networks.

Statistical Estimation and Elimination of Thermal Network Model Errors

1969 ◽  
Vol 91 (4) ◽  
pp. 554-560 ◽  
Author(s):  
B. H. Browne
Keyword(s):  
General Theory ◽  
Complex Systems ◽  
Prediction Accuracy ◽  
Statistical Estimation ◽  
Network Models ◽  
Canonical Forms ◽  
Model Errors ◽  
New Approach ◽  
Thermal Network ◽  
Model Predictions

A new approach is developed for estimating and correcting digital thermal network errors based upon achieving statistical reconciliation between model predictions and observed results. The development is preceded by a review of the general theory of thermal networks and the development of the canonical forms of networks and their error models. Application of the technique promises improved thermal prediction accuracy of complex systems using simplified network models.

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 Enhanced Interconnection Model in Geographically Interdependent Networks

2018 ◽  
Vol 13 (4) ◽  
pp. 537-549
Author(s):  
Diego F. Rueda ◽  
Eusebi Calle ◽  
Xiangrong Wang ◽  
Robert E. Kooij
Keyword(s):  
Realistic Model ◽  
Telecommunication Networks ◽  
Acceptable Level ◽  
Network Robustness ◽  
Critical Infrastructures ◽  
Cascading Failures ◽  
Interdependent Networks ◽  
New Strategy

Interconnection between telecommunication networks and other critical infrastructures is usually established through nodes that are spatially close, generating a geographical interdependency. Previous work has shown that in general, geographically interdependent networks are more robust with respect to cascading failures when the interconnection radius (r) is large. However, to obtain a more realistic model, the allocation of interlinks in geographically interdependent networks should consider other factors. In this paper, an enhanced interconnection model for geographically interdependent networks is presented. The model proposed introduces a new strategy for interconnecting nodes between two geographical networks by limiting the number of interlinks. Results have shown that the model yields promising results to maintain an acceptable level in network robustness under cascading failures with a decrease in the number of interlinks.

scholarly journals Threshold for the Outbreak of Cascading Failures in Degree-Degree Uncorrelated Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Junbiao Liu ◽  
Xinyu Jin ◽  
Lurong Jiang ◽  
Yongxiang Xia ◽  
Bo Ouyang ◽  
...  
Keyword(s):  
Steady State ◽  
Complex Networks ◽  
Large Scale ◽  
Load Sharing ◽  
Network Robustness ◽  
Cascading Failures ◽  
Limited Capacity ◽  
Failure Model ◽  
Theoretical Derivation ◽  
Node Capacity

In complex networks, the size of the giant component formed by unfailed nodes is critically important for estimating the robustness of networks against cascading failures. In order to explore the critical moment of cascading failures break-out, we provide a cascade of overload failure model with local load sharing mechanism and then deduce the threshold of node capacity when the large-scale cascading failures happen and unfailed nodes in steady state cannot connect to each other to form a large connected subnetwork. We get the theoretical derivation of this threshold in degree-degree uncorrelated networks and validate the effectiveness of this method in simulation. This threshold provides us with a guidance to improve the network robustness under the premise of limited capacity resource when creating a network and assigning load. Therefore, this threshold is useful and important to analyze the robustness of networks. We believe that our research provides us with a guidance to improve the network robustness under the premise of limited capacity resource.

scholarly journals Universal behavior of cascading failures in interdependent networks

2019 ◽  
Vol 116 (45) ◽  
pp. 22452-22457 ◽  
Author(s):  
Dongli Duan ◽  
Changchun Lv ◽  
Shubin Si ◽  
Zhen Wang ◽  
Daqing Li ◽  
...  
Keyword(s):  
Real World ◽  
Order Type ◽  
Networked Systems ◽  
Power Grids ◽  
Cascading Failures ◽  
World Systems ◽  
Epidemic Spreading ◽  
Systematic Analysis ◽  
First Order ◽  
Regulatory Dynamics

Catastrophic and major disasters in real-world systems, such as blackouts in power grids or global failures in critical infrastructures, are often triggered by minor events which originate a cascading failure in interdependent graphs. We present here a self-consistent theory enabling the systematic analysis of cascading failures in such networks and encompassing a broad range of dynamical systems, from epidemic spreading, to birth–death processes, to biochemical and regulatory dynamics. We offer testable predictions on breakdown scenarios, and, in particular, we unveil the conditions under which the percolation transition is of the first-order or the second-order type, as well as prove that accounting for dynamics in the nodes always accelerates the cascading process. Besides applying directly to relevant real-world situations, our results give practical hints on how to engineer more robust networked systems.

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.

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