scholarly journals Cascading failures in complex networks

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Lucas D Valdez ◽  
Louis Shekhtman ◽  
Cristian E La Rocca ◽  
Xin Zhang ◽  
Sergey V Buldyrev ◽  
...  
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Real World ◽  
Dynamic Behaviour ◽  
Cascading Failure ◽  
Cascading Failures ◽  
Natural Systems ◽  
Dynamic Propagation ◽  
Open Questions ◽  
Failure Propagation

Abstract Cascading failure is a potentially devastating process that spreads on real-world complex networks and can impact the integrity of wide-ranging infrastructures, natural systems and societal cohesiveness. One of the essential features that create complex network vulnerability to failure propagation is the dependency among their components, exposing entire systems to significant risks from destabilizing hazards such as human attacks, natural disasters or internal breakdowns. Developing realistic models for cascading failures as well as strategies to halt and mitigate the failure propagation can point to new approaches to restoring and strengthening real-world networks. In this review, we summarize recent progress on models developed based on physics and complex network science to understand the mechanisms, dynamics and overall impact of cascading failures. We present models for cascading failures in single networks and interdependent networks and explain how different dynamic propagation mechanisms can lead to an abrupt collapse and a rich dynamic behaviour. Finally, we close the review with novel emerging strategies for containing cascades of failures and discuss open questions that remain to be addressed.

ORIGIN OF THE STRONGER ROBUSTNESS AGAINST CASCADING FAILURES OF COMPLEX NETWORKS: A MITIGATION STRATEGY PERSPECTIVE

2012 ◽  
Vol 27 (03) ◽  
pp. 1350023 ◽  
Author(s):  
JIANWEI WANG
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Load Distribution ◽  
Power Grid ◽  
Mitigation Strategy ◽  
Cascading Failure ◽  
Cascading Failures ◽  
Protection Measures ◽  
Infrastructure Networks ◽  
Initial Load

Cascading failures can occur in many infrastructure networks. How to protect these networks and improve their robustness against cascading failures has been of great interest. To this end, considering that there exist some monitoring and protection measures in these networks, we propose a new mitigation strategy and investigate its effectiveness on improving the robustness level against cascading failures in Barabáasi-Albert (BA) networks and the power grid. We numerically observe that only by once adopting this strategy the robustness of BA networks and the power grid can be improved dramatically. We additionally find that BA networks and the power grid can reach the strongest robustness against cascading failures in the case of the specific value of the parameter α, which controls the strength of the initial load on a node. And we obtain the correlation between the load distribution and the effectiveness of the mitigation strategy. Our findings can well explain the origin of the stronger robustness against cascading failures of complex networks and may be very useful for guiding the improvement robustness of infrastructure networks and avoiding various cascading-failure-induced disasters in the real world.

scholarly journals Power-law distribution of degree–degree distance: A better representation of the scale-free property of complex networks

2020 ◽  
Vol 117 (26) ◽  
pp. 14812-14818 ◽  
Author(s):  
Bin Zhou ◽  
Xiangyi Meng ◽  
H. Eugene Stanley
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Power Law ◽  
Real World ◽  
Preferential Attachment ◽  
Statistical Tests ◽  
Finite Size ◽  
Distance Distribution ◽  
Scale Free ◽  
Degree Distance

Whether real-world complex networks are scale free or not has long been controversial. Recently, in Broido and Clauset [A. D. Broido, A. Clauset,Nat. Commun.10, 1017 (2019)], it was claimed that the degree distributions of real-world networks are rarely power law under statistical tests. Here, we attempt to address this issue by defining a fundamental property possessed by each link, the degree–degree distance, the distribution of which also shows signs of being power law by our empirical study. Surprisingly, although full-range statistical tests show that degree distributions are not often power law in real-world networks, we find that in more than half of the cases the degree–degree distance distributions can still be described by power laws. To explain these findings, we introduce a bidirectional preferential selection model where the link configuration is a randomly weighted, two-way selection process. The model does not always produce solid power-law distributions but predicts that the degree–degree distance distribution exhibits stronger power-law behavior than the degree distribution of a finite-size network, especially when the network is dense. We test the strength of our model and its predictive power by examining how real-world networks evolve into an overly dense stage and how the corresponding distributions change. We propose that being scale free is a property of a complex network that should be determined by its underlying mechanism (e.g., preferential attachment) rather than by apparent distribution statistics of finite size. We thus conclude that the degree–degree distance distribution better represents the scale-free property of a complex network.

Analysis on Power Grid Vulnerability Considering Cascading Failure of Branch

2013 ◽  
Vol 433-435 ◽  
pp. 1254-1257
Author(s):  
Xun Cheng Huang ◽  
Huan Qi ◽  
Xiao Pan Zhang ◽  
Li Fang Lu ◽  
Yang Yu Hu
Keyword(s):  
Complex Networks ◽  
Maximum Degree ◽  
Power Grid ◽  
Maximum Load ◽  
Vulnerability Analysis ◽  
Cascading Failure ◽  
Cascading Failures

This paper analyzes the network features of power grid with buses’ cascading failures based on the theories and methods of complex networks. And it concludes detailed vulnerability analysis of buses in Huazhong power grid under three forms of attack (maximum load attack, maximum degree attack and random attack) .It provides technical means for the prevention of cascading failure .

scholarly journals Characterization of Symmetry of Complex Networks

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 692
Author(s):  
Yangyang Chen ◽  
Yi Zhao ◽  
Xinyu Han
Keyword(s):  
Automorphism Group ◽  
Complex Networks ◽  
Complex Network ◽  
Real World ◽  
Symmetry Property ◽  
Global Symmetry ◽  
Quantitative Characterization ◽  
Natural Action ◽  
Vertex Set

Recently, symmetry in complex network structures has attracted some research interest. One of the fascinating problems is to give measures of the extent to which the network is symmetric. In this paper, based on the natural action of the automorphism group Aut ( Γ ) of Γ on the vertex set V of a given network Γ = Γ ( V , E ) , we propose three indexes for the characterization of the global symmetry of complex networks. Using these indexes, one can get a quantitative characterization of how symmetric a network is and can compare the symmetry property of different networks. Moreover, we compare these indexes to some existing ones in the literature and apply these indexes to real-world networks, concluding that real-world networks are far from vertex symmetric ones.

scholarly journals Identifying Influential Nodes of Complex Networks Based on Trust-Value

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 280
Author(s):  
Jinfang Sheng ◽  
Jiafu Zhu ◽  
Yayun Wang ◽  
Bin Wang ◽  
Zheng’ang Hou
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Real World ◽  
Trust Value ◽  
The Real ◽  
The World ◽  
Attribute Information ◽  
Similarity Ratio ◽  
Influential Nodes ◽  
Influential Node

The real world contains many kinds of complex network. Using influence nodes in complex networks can promote or inhibit the spread of information. Identifying influential nodes has become a hot topic around the world. Most of the existing algorithms used for influential node identification are based on the structure of the network such as the degree of the nodes. However, the attribute information of nodes also affects the ranking of nodes’ influence. In this paper, we consider both the attribute information between nodes and the structure of networks. Therefore, the similarity ratio, based on attribute information, and the degree ratio, based on structure derived from trust-value, are proposed. The trust–PageRank (TPR) algorithm is proposed to identify influential nodes in complex networks. Finally, several real networks from different fields are selected for experiments. Compared with some existing algorithms, the results suggest that TPR more rationally and effectively identifies the influential nodes in networks.

scholarly journals Node diversification in complex networks by decentralized colouring

2018 ◽  
Vol 7 (4) ◽  
pp. 554-563 ◽  
Author(s):  
Richard Garcia-Lebron ◽  
David J Myers ◽  
Shouhuai Xu ◽  
Jie Sun
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Real World ◽  
Local Information ◽  
Connected Component ◽  
Scale Free ◽  
Scale Free Networks

Abstract We develop a decentralized colouring approach to diversify the nodes in a complex network. The key is the introduction of a local conflict index (LCI) that measures the colour conflicts arising at each node which can be efficiently computed using only local information. We demonstrate via both synthetic and real-world networks that the proposed approach significantly outperforms random colouring as measured by the size of the largest colour-induced connected component. Interestingly, for scale-free networks further improvement of diversity can be achieved by tuning a degree-biasing weighting parameter in the LCI.

Uncovering the fuzzy community structure accurately based on steepest descent projection

2017 ◽  
Vol 31 (27) ◽  
pp. 1750249 ◽  
Author(s):  
Changjian Fang ◽  
Dejun Mu ◽  
Zhenghong Deng ◽  
Jiaqi Yan
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Complex Network ◽  
Real World ◽  
Large Scale ◽  
Steepest Descent ◽  
Topological Information ◽  
Generalized Framework ◽  
Open Question ◽  
Fuzzy C Means Algorithm

Uncovering the community structure in complex network is a hot research point in recent years. How to identify the community structure accurately in complex network is still an open question under research. There are lots of methods based on topological information, which have some good performances at the expense of longer runtimes. In this paper, we propose a new fuzzy algorithm which follows the line of fuzzy c-means algorithm. A steepest descent framework with projection by optimizing the quality function is presented under the generalized framework. The results of experiments on both real-world networks and synthetic networks show that the proposed method achieves the highest efficiency and is easy for detecting fuzzy community structure in large-scale complex networks.

scholarly journals Study of Cascading Failure in Multisubnet Composite Complex Networks

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 523
Author(s):  
Gengxin Sun ◽  
Chih-Cheng Chen ◽  
Sheng Bin
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Influencing Factors ◽  
Network Models ◽  
Network Robustness ◽  
Cascading Failure ◽  
Network System ◽  
Multiple Relationships ◽  
Hierarchical Network ◽  
Coupling Relationship

Current research on the cascading failure of coupling networks is mostly based on hierarchical network models and is limited to a single relationship. In reality, many relationships exist in a network system, and these relationships collectively affect the process and scale of the network cascading failure. In this paper, a composite network is constructed based on the multisubnet composite complex network model, and its cascading failure is proposed combined with multiple relationships. The effect of intranetwork relationships and coupling relationships on network robustness under different influencing factors is studied. It is shown that cascading failure in composite networks is different from coupling networks, and increasing the strength of the coupling relationship can significantly improve the robustness of the network.

A New Evaluation Method of Node Importance in Directed Weighted Complex Networks

2017 ◽  
Vol 5 (4) ◽  
pp. 367-375 ◽  
Author(s):  
Yu Wang ◽  
Jinli Guo ◽  
Han Liu
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Real World ◽  
Evaluation Method ◽  
Network Models ◽  
Node Importance ◽  
Weighted Complex Network ◽  
Complex Network Models ◽  
Evaluation Matrix ◽  
Key Nodes

AbstractCurrent researches on node importance evaluation mainly focus on undirected and unweighted networks, which fail to reflect the real world in a comprehensive and objective way. Based on directed weighted complex network models, the paper introduces the concept of in-weight intensity of nodes and thereby presents a new method to identify key nodes by using an importance evaluation matrix. The method not only considers the direction and weight of edges, but also takes into account the position importance of nodes and the importance contributions of adjacent nodes. Finally, the paper applies the algorithm to a microblog-forwarding network composed of 34 users, then compares the evaluation results with traditional methods. The experiment shows that the method proposed can effectively evaluate the node importance in directed weighted networks.

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