EFFECT OF CLIENT DEMANDS ON DESTRUCTIVENESS OF TARGETED ATTACKS IN DIRECTED WEIGHTED SCALE-FREE NETWORKS

2011 ◽  
Vol 25 (19) ◽  
pp. 1603-1617 ◽  
Author(s):  
LI-LI MA ◽  
XIN JIANG ◽  
ZHAN-LI ZHANG ◽  
ZHI-MING ZHENG
Keyword(s):  
Real World ◽  
Weight Distribution ◽  
Network Robustness ◽  
Network Nodes ◽  
Scale Free ◽  
Node Importance ◽  
Targeted Attacks ◽  
Link Weight ◽  
Scale Free Networks ◽  
Unique Mechanism

Network resilience is vital for the survival of networks, and scale-free networks are fragile when confronted with targeted attacks. We survey network robustness to targeted attacks from the viewpoint of network clients by designing a unique mechanism based on the undeniable roles of network clients in real-world networks. Especially, the mechanism here is designed on the actual phenomenon that the vital nodes in a network may be totally different for clients with different demands. Concretely, node client-demand centrality is proposed to quantify the contributions of nodes to network clients and we show that it is a proper index to assign an order to network nodes according to node importance for network clients. Great discrepancy of node importance order for clients with different demands is found in scale-free networks with four different kinds of link weight distribution, which suggests that the destructiveness of fatal attacks on networks can be greatly reduced by adjusting the demands of network clients.

scholarly journals A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhaoxing Li ◽  
Qionghai Liu ◽  
Li Chen
Keyword(s):  
Multiobjective Optimization ◽  
Complex Networks ◽  
Optimization Algorithm ◽  
Real World ◽  
Network Robustness ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Scale Free ◽  
Scale Free Networks ◽  
The Given

A complex network can crash down due to disturbances which significantly reduce the network’s robustness. It is of great significance to study on how to improve the robustness of complex networks. In the literature, the network rewire mechanism is one of the most widely adopted methods to improve the robustness of a given network. Existing network rewire mechanism improves the robustness of a given network by re-connecting its nodes but keeping the total number of edges or by adding more edges to the given network. In this work we propose a novel yet efficient network rewire mechanism which is based on multiobjective optimization. The proposed rewire mechanism simultaneously optimizes two objective functions, i.e., maximizing network robustness and minimizing edge rewire operations. We further develop a multiobjective discrete partite swarm optimization algorithm to solve the proposed mechanism. Compared to existing network rewire mechanisms, the developed mechanism has two advantages. First, the proposed mechanism does not require specific constraints on the rewire mechanism to the studied network, which makes it more feasible for applications. Second, the proposed mechanism can suggest a set of network rewire choices each of which can improve the robustness of a given network, which makes it be more helpful for decision makings. To validate the effectiveness of the proposed mechanism, we carry out experiments on computer-generated Erdős–Rényi and scale-free networks, as well as real-world complex networks. The results demonstrate that for each tested network, the proposed multiobjective optimization based edge rewire mechanism can recommend a set of edge rewire solutions to improve its robustness.

scholarly journals Network robustness improvement via long-range links

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincenza Carchiolo ◽  
Marco Grassia ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni
Keyword(s):  
Complex Networks ◽  
Long Range ◽  
Real World ◽  
Network Robustness ◽  
Scale Free ◽  
Area Of Interest ◽  
Before And After ◽  
Significant Area ◽  
Better Than

AbstractMany systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.

scholarly journals NONUNIVERSALITY IN SEMI-DIRECTED BARABÁSI–ALBERT NETWORKS

2012 ◽  
Vol 23 (09) ◽  
pp. 1250062 ◽  
Author(s):  
M. A. SUMOUR ◽  
M. A. RADWAN
Keyword(s):  
Type A ◽  
Network Nodes ◽  
Scale Free ◽  
Scale Free Networks ◽  
Usual Scale

In usual scale-free networks of Barabási–Albert type, a newly added node selects randomly m neighbors from the already existing network nodes, proportionally to the number of links these had before. Then the number n(k) of nodes with k links each decays as 1/kγ where γ = 3 is universal, i.e. independent of m. Now we use a limited directedness in building the network, as a result of which the exponent γ decreases from 3 to 2 for increasing m.

scholarly journals OPTIMIZATION OF ROBUSTNESS OF SCALE-FREE NETWORK TO RANDOM AND TARGETED ATTACKS

2005 ◽  
Vol 19 (16) ◽  
pp. 785-792 ◽  
Author(s):  
JIAN-GUO LIU ◽  
ZHONG-TUO WANG ◽  
YAN-ZHONG DANG
Keyword(s):  
Network Design ◽  
Scale Free Network ◽  
Design Guideline ◽  
Scale Free ◽  
Targeted Attacks ◽  
Maximum Value ◽  
Scale Free Networks ◽  
Free Network ◽  
Random Failures ◽  
Connectivity Distribution

Scale-free networks, having connectivity distribution P(k)~k-α (where k is the site connectivity), are very resilient to random failures but are fragile to intentional attacks. The purpose of this paper is to find the network design guideline which can make the robustness of the network to both random failures and intentional attacks maximum while keeping the average connectivity <k> per node constant. We find that when <k> = 3 the robustness of the scale-free networks reach its maximum value if the minimal connectivity m = 1, but when <k> is larger than four, the networks will become more robust to random failures and targeted attacks as the minimal connectivity m gets larger.

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.

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.

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.

EXACT SOLUTIONS FOR AVERAGE TRAPPING TIME OF RANDOM WALKS ON WEIGHTED SCALE-FREE NETWORKS

Fractals ◽  
2017 ◽  
Vol 25 (02) ◽  
pp. 1750013 ◽  
Author(s):  
CHANGMING XING ◽  
YIGONG ZHANG ◽  
JUN MA ◽  
LIN YANG ◽  
LEI GUO
Keyword(s):  
Random Walks ◽  
Exact Solutions ◽  
Weight Distribution ◽  
Close Relation ◽  
The Other ◽  
Trapping Time ◽  
Scale Free ◽  
Weight Parameter ◽  
Scale Free Networks ◽  
Fractal Network

In this paper, we present two deterministic weighted scale-free networks controlled by a weight parameter [Formula: see text]. One is fractal network, the other one is non-fractal network, while they have the same weight distribution when the parameter [Formula: see text] is identical. Based on their special network structure, we study random walks on network with a trap located at a fixed node. For each network, we calculate exact solutions for average trapping time (ATT). Analyzing and comparing the obtained solutions, we find that their ATT all grow asymptotically as a power-law function of network order (number of nodes) with the exponent [Formula: see text] dependent on the weight parameter, but their exponent [Formula: see text] are obviously different, one is an increasing function of [Formula: see text], while the other is opposite. Collectively, all the obtained results show that the efficiency of trapping on weighted Scale-free networks has close relation to the weight distribution, but there is no stable positive or negative correlation between the weight distribution and the trapping time on different networks. We hope these results given in this paper could help us get deeper understanding about the weight distribution on the property and dynamics of scale-free networks.

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