scholarly journals A Survey on Frameworks Used for Robustness Analysis on Interdependent Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Ivana Bachmann ◽  
Javier Bustos-Jiménez ◽  
Benjamin Bustos
Keyword(s):  
Robustness Analysis ◽  
Network Models ◽  
Network Systems ◽  
Embedded Networks ◽  
Interdependent Networks ◽  
Interdependent Network ◽  
Interacting Networks ◽  
Robustness Metrics ◽  
Synthetic Models ◽  
Multiple Interactions

The analysis of network robustness tackles the problem of studying how a complex network behaves under adverse scenarios, such as failures or attacks. In particular, the analysis of interdependent networks’ robustness focuses on the specific case of the robustness of interacting networks and their emerging behaviors. This survey systematically reviews literature of frameworks that analyze the robustness of interdependent networks published between 2005 and 2017. This review shows that there exists a broad range of interdependent network models, robustness metrics, and studies that can be used to understand the behaviour of different systems under failure or attack. Regarding models, we found that there is a focus on systems where a node in one layer interacts with exactly one node at another layer. In studies, we observed a focus on the network percolation. While among the metrics, we observed a focus on measures that count network elements. Finally, for the networks used to test the frameworks, we found that the focus was on synthetic models, rather than analysis of real network systems. This review suggests opportunities in network research, such as the study of robustness on interdependent networks with multiple interactions and/or spatially embedded networks, and the use of interdependent network models in realistic network scenarios.

scholarly journals Improvement on Load-Induced Cascading Failure in Asymmetrical Interdependent Networks: Modeling and Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Haiyan Han ◽  
Rennong Yang
Keyword(s):  
Load Capacity ◽  
Small World ◽  
Cascading Failure ◽  
Modeling And Analysis ◽  
Interdependent Networks ◽  
Interdependent Network ◽  
Capacity Model ◽  
Weight Structure ◽  
Edge Failure ◽  
Asymmetrical Model

Many real-world systems can be depicted as interdependent networks and they usually show an obvious property of asymmetry. Furthermore, node or edge failure can trigger load redistribution which leads to a cascade of failure in the whole network. In order to deeply investigate the load-induced cascading failure, firstly, an asymmetrical model of interdependent network consisting of a hierarchical weighted network and a WS small-world network is constructed. Secondly, an improved “load-capacity” model is applied for node failure and edge failure, respectively, followed by a series of simulations of cascading failure over networks in both interdependent and isolated statuses. The simulation results prove that the robustness in isolated network changes more promptly than that in the interdependent one. Network robustness is positively related to “capacity,” but negatively related to “load.” The hierarchical weight structure in the subnetwork leads to a “plateau” phenomenon in the progress of cascading failure.

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 Between Imitation and Embeddedness: Three Types of Polish Alternative Food Networks

2019 ◽  
Vol 11 (24) ◽  
pp. 7059 ◽  
Author(s):  
Wojciech Goszczyński ◽  
Ruta Śpiewak ◽  
Aleksandra Bilewicz ◽  
Michał Wróblewski
Keyword(s):  
Secondary Analysis ◽  
Network Models ◽  
Social Contexts ◽  
Alternative Food ◽  
Alternative Food Networks ◽  
Embedded Networks ◽  
The Past ◽  
The Social ◽  
Food Networks ◽  
Mixed Networks

The purpose of this article is to present the specific character of Alternative Food Networks (AFNs) in Poland as one of the countries of Central and Eastern Europe (CEE). We refer to the issue increasingly debated in the social sciences, that is, how to translate academic models embedded in specific social contexts to other contexts, as we trace the process of adapting ideas and patterns of AFNs developed in the West to the semi-peripheral context of CEE countries. Drawing on the theory of social practices, we divide the analysis into three essential areas: The ideas of the network, its materiality, and the activities within the network. We have done secondary analysis of the research material, including seven case studies the authors worked on in the past decade. We distinguish three network models—imitated, embedded and mixed—which allow us to establish a specific post-transformational AFN growth theory. Particular attention should be paid to the type of embedded networks, as they highlight the possibility of local and original forms of AFNs. Mixed networks show that ideas imported from abroad need to be considered in juxtaposition and connection with local circumstances.

The Structure of Vulnerable Nodes in Behavioral Network Models of Complex Engineered Systems

2017 ◽  
Author(s):  
Hannah S. Walsh ◽  
Andy Dong ◽  
Irem Y. Tumer
Keyword(s):  
Network Models ◽  
Design Parameters ◽  
Engineering Systems ◽  
Critical Design ◽  
Complex Engineered Systems ◽  
Design Variables ◽  
Engineered Systems ◽  
Process Controls ◽  
Robustness Metrics

All methods associated with failure analysis attempt to identify critical design variables and parameters such that appropriate process controls can be implemented to detect problems before they occur. This paper introduces a new approach to the identification of critical design variables and parameters through the concept of bridging nodes. Using a network-based perspective in which design parameters and variables are modeled as nodes, results show that vulnerable parameters tend to be bridging nodes, which are nodes that connect two or more groups of nodes that are organized together in order to perform an intended function. This paper extends existing modeling capabilities based upon a behavioral network analysis (BNA) approach and presents empirical results identifying the relationship between bridging nodes and parameter vulnerability as determined by existing, network metric-based methods. These topological network robustness metrics were used to analyze a large number of engineering systems. Bridging nodes are associated with significantly larger changes in network degradation, as measured by these metrics, than non-bridging nodes when subject to attack (p < 0.001). The results indicate the structural role of vulnerable design parameters in a behavioral network.

scholarly journals Structural Models of Cortical Networks with Long-Range Connectivity

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Nicole Voges ◽  
Ad Aertsen ◽  
Stefan Rotter
Keyword(s):  
Graph Theory ◽  
Long Range ◽  
Neuronal Activity ◽  
Structural Models ◽  
Network Models ◽  
Embedded Networks ◽  
Stochastic Graph ◽  
Random Connectivity ◽  
Activity Dynamics ◽  
Specific Distance

Most current studies of neuronal activity dynamics in cortex are based on network models with completely random wiring. Such models are chosen for mathematical convenience, rather than biological grounds, and additionally reflect the notorious lack of knowledge about the neuroanatomical microstructure. Here, we describe some families of new, more realistic network models and explore some of their properties. Specifically, we consider spatially embedded networks and impose specific distance-dependent connectivity profiles. Each of these network models can cover the range from purely local to completely random connectivity, controlled by a single parameter. Stochastic graph theory is then used to describe and analyze the structure and the topology of these networks.

scholarly journals Structural Robustness of Unidirectional Dependent Networks Based on Attack Strategies

2016 ◽  
Vol 16 (6) ◽  
pp. 175-184
Author(s):  
Longbang Ma ◽  
Ping Guo ◽  
Juan Zhao ◽  
Lei Qi
Keyword(s):  
Interdependent Networks ◽  
Structural Robustness ◽  
High Betweenness ◽  
Interacting Networks

Abstract Current works have been focused on the robustness of single network and interdependent networks. However, to be more correct, the dependence of many real systems should be described as unidirectional. To study the structural robustness of networks with unidirectional dependence, the dependent networks named UDN are proposed, the description of the propagation of failures in them is given, as well as the introduction of the attack strategies that the probability of a node being attacked depends on the degree (DP attack) or on the betweenness (BP attack) of this node. The simulated results show that UDN is more vulnerable to BP attack when is first attacked a node with high betweenness. Compared with the Interacting Networks (IN), the UDN is more fragile under the two attack’s strategies.

scholarly journals Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Weisong Zhou ◽  
Zhichun Yang
Keyword(s):  
Sufficient Conditions ◽  
Network Models ◽  
Functional Method ◽  
Linear Matrix ◽  
Time Varying ◽  
Neural Network Models ◽  
Network Systems ◽  
Nonlinear Network ◽  
Dynamical Neural Network ◽  
Dynamical Neural Networks

A class of dynamical neural network models with time-varying delays is considered. By employing the Lyapunov-Krasovskii functional method and linear matrix inequalities (LMIs) technique, some new sufficient conditions ensuring the input-to-state stability (ISS) property of the nonlinear network systems are obtained. Finally, numerical examples are provided to illustrate the efficiency of the derived results.

scholarly journals Peer pressure induced punishment resolves social dilemma on interdependent networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kaipeng Hu ◽  
Yewei Tao ◽  
Yongjuan Ma ◽  
Lei Shi
Keyword(s):  
Random Network ◽  
Social Dilemma ◽  
Small World ◽  
Peer Pressure ◽  
Evolution Of Cooperation ◽  
Interdependent Networks ◽  
Interdependent Network ◽  
Psychological Harm ◽  
Will Force ◽  
The Impact

AbstractDespite the fruitful evidence to support the emergence of cooperation, irrational decisions are still an essential part of promoting cooperation. Among the many factors that affect human rational decision-making, peer pressure is unique to social organisms and directly affects individual cooperative behaviors in the process of social interaction. This kind of pressure psychologically forces individuals to behave consistently with their partners, and partners with inconsistent behaviors may suffer psychological blows. As feedback, this psychological harm may in turn affect individual cooperative decisions. There is evidence that when peer pressure exists, partnerships can reduce free-riding in enterprise. Based on interdependent networks, this paper studies the impact of peer pressure on cooperation dynamics when the strategies of corresponding partners from different layers of the networks are inconsistent. We assume that when individuals are under peer pressure, their payoffs will be compromised. The simulation results show that the punishment effect will force the expulsion of partners with different strategies, which will further reduce the proportion of partners with inconsistent strategies in the system. However, in most cases, only moderate fines are most conductive to the evolution of cooperation, and the punishment mechanisms can effectively promote the interdependent network reciprocity. The results on the small world and random network prove the robustness of the result. In addition, under this mechanism, the greater the payoff dependence between partners, the better the effect of interdependent network reciprocity.

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