Cascading failures in interdependent modular networks with partial random coupling preference

2017 ◽  
Vol 31 (29) ◽  
pp. 1750267 ◽  
Author(s):  
Meng Tian ◽  
Xianpei Wang ◽  
Zhengcheng Dong ◽  
Guowei Zhu ◽  
Jiachuang Long ◽  
...  
Keyword(s):  
Community Structure ◽  
Network Robustness ◽  
Cascading Failures ◽  
Scale Free ◽  
Interdependent Networks ◽  
Connection Probability ◽  
Random Coupling ◽  
Protection Cost ◽  
Interdependent Infrastructures ◽  
Partial Coupling

Cascading failures have been widely analyzed in interdependent networks with different coupling preferences from microscopic and macroscopic perspectives in recent years. Plenty of real-world interdependent infrastructures, representing as interdependent networks, exhibit community structure, one of the most important mesoscopic structures, and partial coupling preferences, which can affect cascading failures in interdependent networks. In this paper, we propose the partial random coupling in communities, investigating cascading failures in interdependent modular scale-free networks under inner attacks and hub attacks. We mainly analyze the effects of the discoupling probability and the intermodular connection probability on cascading failures in interdependent networks. We find that increasing either the dicoupling probability or the intermodular connection probability can enhance the network robustness under both hub attacks and inner attacks. We also note that the community structure can prevent cascading failures spreading globally in entire interdependent networks. Finally, we obtain the result that if we want to efficiently improve the robustness of interdependent networks and reduce the protection cost, the intermodular connection probability should be protected preferentially, implying that improving the robustness of a single network is the fundamental method to enhance the robustness of the entire interdependent networks.

Cascading failures of interdependent networks with different k-core structures

2017 ◽  
Vol 31 (10) ◽  
pp. 1750112 ◽  
Author(s):  
Zhengcheng Dong ◽  
Yanjun Fang ◽  
Meng Tian
Keyword(s):  
Real Life ◽  
Network Evolution ◽  
Cascading Failures ◽  
Scale Free Network ◽  
Scale Free ◽  
Interdependent Networks ◽  
Mesoscale Structures ◽  
Initial Load ◽  
Free Network ◽  
Random Coupling

As one of the most common mesoscale structures in real-life networks, k-core hierarchical structure has attracted a lot of attention. Recent research about k-core always focuses on detecting influential nodes determining failure or epidemic propagation. However, few studies have attempted to understand how k-core structural properties can affect dynamic characteristics of network. In this paper, the influences of depth and coupling preferences of k-core on the cascading failures of interdependent scale-free networks are investigated. First, k-core structures of some real-life networks are analyzed, and a scale-free network evolution model with rich and successive k-core layers is proposed. Then, based on a load-based cascading model, the influence of the depth of k-core is investigated with a new evaluation index. In the end, two coupling preferences are analyzed, i.e. random coupling (RC) and assortative coupling (AC). Results show that the lower the depth is, the more robust the interdependent networks will be, and we find AC and RC perform dissimilarly when the capacity varies. Furthermore, all the effects will be affected by the initial load.

scholarly journals Cascading failures in scale-free interdependent networks

2019 ◽  
Vol 99 (3) ◽  
Author(s):  
Malgorzata Turalska ◽  
Keith Burghardt ◽  
Martin Rohden ◽  
Ananthram Swami ◽  
Raissa M. D'Souza
Keyword(s):  
Cascading Failures ◽  
Scale Free ◽  
Interdependent Networks

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.

Cascading failures in complex networks with community structure

2014 ◽  
Vol 25 (05) ◽  
pp. 1440005 ◽  
Author(s):  
Guoqiang Lin ◽  
Zengru Di ◽  
Ying Fan
Keyword(s):  
Community Structure ◽  
Community Networks ◽  
Random Networks ◽  
Cascading Failures ◽  
Power Law Distribution ◽  
Avalanche Size ◽  
Scale Free ◽  
High Betweenness ◽  
Failure Scenario ◽  
The Relationship

Much empirical evidence shows that when attacked with cascading failures, scale-free or even random networks tend to collapse more extensively when the initially deleted node has higher betweenness. Meanwhile, in networks with strong community structure, high-betweenness nodes tend to be bridge nodes that link different communities, and the removal of such nodes will reduce only the connections among communities, leaving the networks fairly stable. Understanding what will affect cascading failures and how to protect or attack networks with strong community structure is therefore of interest. In this paper, we have constructed scale-free Community Networks (SFCN) and Random Community Networks (RCN). We applied these networks, along with the Lancichinett–Fortunato–Radicchi (LFR) benchmark, to the cascading-failure scenario to explore their vulnerability to attack and the relationship between cascading failures and the degree distribution and community structure of a network. The numerical results show that when the networks are of a power-law distribution, a stronger community structure will result in the failure of fewer nodes. In addition, the initial removal of the node with the highest betweenness will not lead to the worst cascading, i.e. the largest avalanche size. The Betweenness Overflow (BOF), an index that we developed, is an effective indicator of this tendency. The RCN, however, display a different result. In addition, the avalanche size of each node can be adopted as an index to evaluate the importance of the node.

scholarly journals Modularity affects the robustness of scale-free model and real-world social networks under betweenness and degree-based node attack

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Quang Nguyen ◽  
Tuan V. Vu ◽  
Hanh-Duyen Dinh ◽  
Davide Cassi ◽  
Francesco Scotognella ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Real World ◽  
Ad Hoc ◽  
Modular Structure ◽  
Network Robustness ◽  
Node Degree ◽  
Percolation Transition ◽  
Scale Free ◽  
Model Networks

AbstractIn this paper we investigate how the modularity of model and real-world social networks affect their robustness and the efficacy of node attack (removal) strategies based on node degree (ID) and node betweenness (IB). We build Barabasi–Albert model networks with different modularity by a new ad hoc algorithm that rewire links forming networks with community structure. We traced the network robustness using the largest connected component (LCC). We find that when model networks present absent or low modular structure ID strategy is more effective than IB to decrease the LCC. Conversely, in the case the model network present higher modularity, the IB strategy becomes the most effective to fragment the LCC. In addition, networks with higher modularity present a signature of a 1st order percolation transition and a decrease of the LCC with one or several abrupt changes when nodes are removed, for both strategies; differently, networks with non-modular structure or low modularity show a 2nd order percolation transition networks when nodes are removed. Last, we investigated how the modularity of the network structure evaluated by the modularity indicator (Q) affect the network robustness and the efficacy of the attack strategies in 12 real-world social networks. We found that the modularity Q is negatively correlated with the robustness of the real-world social networks for both the node attack strategies, especially for the IB strategy (p-value < 0.001). This result indicates how real-world networks with higher modularity (i.e. with higher community structure) may be more fragile to node attack. The results presented in this paper unveil the role of modularity and community structure for the robustness of networks and may be useful to select the best node attack strategies in network.

Attack robustness of cascading load model in interdependent networks

2015 ◽  
Vol 26 (03) ◽  
pp. 1550030 ◽  
Author(s):  
Jianwei Wang ◽  
Yuedan Wu ◽  
Yun Li
Keyword(s):  
The Other ◽  
Network Robustness ◽  
Cascading Failures ◽  
Load Model ◽  
Interdependent Networks ◽  
Reasonable Explanation ◽  
Targeted Attacks ◽  
Initial Load ◽  
Lower Load ◽  
Local Perspective

Considering the weight of a node and the coupled strength of two interdependent nodes in the different networks, we propose a method to assign the initial load of a node and construct a new cascading load model in the interdependent networks. Assuming that a node in one network will fail if its degree is 0 or its dependent node in the other network is removed from the network or the load on it exceeds its capacity, we study the influences of the assortative link (AL) and the disassortative link (DL) patterns between two networks on the robustness of the interdependent networks against cascading failures. For better evaluating the network robustness, from the local perspective of a node we present a new measure to qualify the network resiliency after targeted attacks. We show that the AL patterns between two networks can improve the robust level of the entire interdependent networks. Moreover, we obtain how to efficiently allocate the initial load and select some nodes to be protected so as to maximize the network robustness against cascading failures. In addition, we find that some nodes with the lower load are more likely to trigger the cascading propagation when the distribution of the load is more even, and also give the reasonable explanation. Our findings can help to design the robust interdependent networks and give the reasonable suggestion to optimize the allocation of the protection resources.

An efficient local cascade defense method in complex networks

2017 ◽  
Vol 28 (03) ◽  
pp. 1750031 ◽  
Author(s):  
Zhong-Yuan Jiang ◽  
Jian-Feng Ma
Keyword(s):  
Network Robustness ◽  
Local Load ◽  
Cascading Failures ◽  
Spare Capacity ◽  
Single Node ◽  
Scale Free ◽  
Load Redistribution ◽  
Failure Propagation ◽  
Average Distribution ◽  
Failed Node

Cascading failures in networked systems often lead to catastrophic consequence. Defending cascading failure propagation by employing local load redistribution method is an efficient way. Given initial load of every node, the key of improving network robustness against cascading failures is to maximally defend cascade propagation with minimum total extra capacity of all nodes. With finite total extra capacity of all nodes, we first discuss three general extra capacity distributions including degree-based distribution (DD), average distribution (AD) and random distribution (RD). To sufficiently use the total spare capacity (SC) of all neighboring nodes of a failed node, then we propose a novel SC-based local load redistribution mechanism to improve the cascade defense ability of network. We investigate the network robustness against cascading failures induced by a single node failure under the three extra capacity distributions in both scale-free networks and random networks. Compared with the degree-based (DB) local load redistribution method, our SC method achieves higher robustness under all of the three extra capacity distributions. The extensive simulation results can well confirm the effectiveness of the SC local load redistribution method.

Improving the robustness of scale-free networks by maintaining community structure

2019 ◽  
Vol 7 (6) ◽  
pp. 838-864 ◽  
Author(s):  
Marzieh Mozafari ◽  
Mohammad Khansari
Keyword(s):  
Community Structure ◽  
Degree Distribution ◽  
Network Performance ◽  
Network Robustness ◽  
Degree Centrality ◽  
Functional Modules ◽  
Scale Free ◽  
Scale Free Networks ◽  
Artificial Network ◽  
Attack Strategy

Abstract Scale-free networks, which play an important role in modelling human activities, are always suffering from intentional attacks which have serious consequences on their functionality. Degree distribution and community structure are two distinguishing characteristics of these networks considered in optimizing network robustness process recently. Since community structure is known as functional modules in some networks, modifying them during the improving network robustness process may affect network performance. We propose a preferential rewiring method to improve network robustness which not only keeps degree distribution unchanged but also preserves community structure and decreases the number of rewired edges at the same time. At first, the robustness of each community is improved by applying a smart rewiring method based on the neighbourhood of nodes. Then, relations between communities are gotten more robust with a preferential rewiring based on degree and betweenness hybrid centrality of nodes. This method was applied to several types of networks including Dolphins, WU-PowerGrid and US-Airline as real-world networks and Lancichinetti–Fortunato–Radicchi benchmark model as an artificial network with the scale-free property. The results show that the proposed method enhances the robustness of all networks against degree centrality attacks between 50% and 80% and betweenness centrality attacks between 30% and 70%. Whereas, in all cases, community structure preserved more than 50%. In comparison with previous studies, the proposed method can improve network robustness more significantly and decrease the number of rewires. It also is not dependent on the attack strategy.

VULNERABILITY OF EFFECTIVE ATTACK ON EDGES IN SCALE-FREE NETWORKS DUE TO CASCADING FAILURES

2009 ◽  
Vol 20 (08) ◽  
pp. 1291-1298 ◽  
Author(s):  
JIAN-WEI WANG ◽  
LI-LI RONG
Keyword(s):  
Theoretical Prediction ◽  
Numerical Simulations ◽  
Real Life ◽  
Network Robustness ◽  
Critical Threshold ◽  
Cascading Failure ◽  
Cascading Failures ◽  
Scale Free ◽  
Scale Free Networks ◽  
Initial Load

Assume the initial load of an edge ij in a network to be Lij =[(ki ∑a ∈ Γi ka)(kj ∑b ∈ Γj kb)]α with ki and kj being the degrees of the nodes connected by the edge, where α is a tunable parameter which controls the strength of the edge initial load, and Γi and Γj are the sets of neighboring nodes of i and j, respectively. We investigate the cascading phenomenon of uncorrelated scale-free networks subject to two different attacking strategies on edges, i.e. attacking on the edges with the highest loads or the lowest loads (LL). By the critical threshold of edge capacity quantifying the network robustness, we numerically discuss the effects of two attacks for the network vulnerability. Interestingly, it is found that the attack on the edge with the LL is highly effective in disrupting the structure of the attacked network when α < 0.5. In the case of α = 0.5, the effects of two attacks for the network robustness against cascading failures are almost identical. We furthermore provide the theoretical prediction support for the numerical simulations. These results may be very helpful for real-life networks to protect the key edges selected effectively to avoid cascading-failure-induced disasters.

Robustness of complex networks with both unidirectional and bidirectional links against cascading failures

2017 ◽  
Vol 31 (27) ◽  
pp. 1750252 ◽  
Author(s):  
Lin Ding ◽  
Victor C. M. Leung ◽  
Min-Sheng Tan
Keyword(s):  
Complex Networks ◽  
Low Cost ◽  
Small World ◽  
Network Robustness ◽  
Cascading Failures ◽  
Small World Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
Simulation Results ◽  
Direction Determining

The robustness of complex networks against cascading failures has been of great interest, while most of the researchers have considered undirected networks. However, to be more realistic, a part of links of many real systems should be described as unidirectional. In this paper, by applying three link direction-determining (DD) strategies, the tolerance of cascading failures is investigated in various networks with both unidirectional and bidirectional links. By extending the utilization of a classical global betweenness method, we propose a new cascading model, taking into account the weights of nodes and the directions of links. Then, the effects of unidirectional links on the network robustness against cascaded attacks are examined under the global load-based distribution mechanism. The simulation results show that the link-directed methods could not always lead to the decrease of the network robustness as indicated in the previous studies. For small-world networks, these methods certainly make the network weaker. However, for scale-free networks, the network robustness can be significantly improved by the link-directed method, especially for the method with non-random DD strategies. These results are independent of the weight parameter of the nodes. Due to the strongly improved robustness and easy realization with low cost on networks, the method for enforcing proper links to the unidirectional ones may be useful for leading to insights into the control of cascading failures in real-world networks, like communication and transportation networks.

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