scholarly journals Combined Heuristic Attack Strategy on Complex Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Marek Šimon ◽  
Iveta Dirgová Luptáková ◽  
Ladislav Huraj ◽  
Marián Hosťovecký ◽  
Jiří Pospíchal
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Biological Networks ◽  
Case Scenario ◽  
Worst Case ◽  
Scale Free ◽  
Power Law Exponent ◽  
Spreading Disease ◽  
Heuristic Criterion ◽  
Attack Strategy

Usually, the existence of a complex network is considered an advantage feature and efforts are made to increase its robustness against an attack. However, there exist also harmful and/or malicious networks, from social ones like spreading hoax, corruption, phishing, extremist ideology, and terrorist support up to computer networks spreading computer viruses or DDoS attack software or even biological networks of carriers or transport centers spreading disease among the population. New attack strategy can be therefore used against malicious networks, as well as in a worst-case scenario test for robustness of a useful network. A common measure of robustness of networks is their disintegration level after removal of a fraction of nodes. This robustness can be calculated as a ratio of the number of nodes of the greatest remaining network component against the number of nodes in the original network. Our paper presents a combination of heuristics optimized for an attack on a complex network to achieve its greatest disintegration. Nodes are deleted sequentially based on a heuristic criterion. Efficiency of classical attack approaches is compared to the proposed approach on Barabási-Albert, scale-free with tunable power-law exponent, and Erdős-Rényi models of complex networks and on real-world networks. Our attack strategy results in a faster disintegration, which is counterbalanced by its slightly increased computational demands.

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.

The Scale-Free Characteristics of the Micro-Blog Community

2013 ◽  
Vol 278-280 ◽  
pp. 2118-2122
Author(s):  
Bo Wang ◽  
Meng Jia Zhao ◽  
Sheng Li
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Government Policies ◽  
Scale Free ◽  
Scale Free Networks

The micro-blog community is the complex composed of many nodes. Based on interrelated theory of complex networks, this paper constructs the micro-blog community complex network by taking users as nodes, relationship between users as edges, then analyzes the scale-free features and shows that micro-blog community fits the two main characteristics of scale-free networks. Finally from the aspects of government policies、operators and users, putting forward some suggestions about micro-blog community according to its scale-free characteristics in order to let micro-blog play a better role in society.

scholarly journals Modelling and analysis of transportation networks using complex networks: Poland case study

2015 ◽  
Vol 36 (4) ◽  
pp. 55-65 ◽  
Author(s):  
Zbigniew Tarapata
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Transportation Networks ◽  
Small World ◽  
Scale Free ◽  
Empirical Results

In the paper a theoretical bases and empirical results deal with analysis and modelling of transportation networks in Poland using complex networks have been presented. Properties of complex networks (Scale Free and Small World) and network's characteristic measures have been described. In this context, results of empirical researches connected with characteristics of passenger air links network, express railway links network (EuroCity and InterCity) and expressways/highways network in Poland have been given. For passenger air links network in Poland results are compared with the same networks in USA, China, India, Italy and Spain. In the conclusion some suggestions, observations and perspective dealing with complex network in transportation networks have been presented.

scholarly journals Controllability and Optimization of Complex Networks Based on Bridges

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Lifu Wang ◽  
Guotao Zhao ◽  
Zhi Kong ◽  
Yunkang Zhao
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Betweenness Centrality ◽  
The Other ◽  
Connected Components ◽  
Scale Free ◽  
Scale Free Networks ◽  
Model Networks ◽  
Simulation Results ◽  
And Control

In a complex network, each edge has different functions on controllability of the whole network. A network may be out of control due to failure or attack of some specific edges. Bridges are a kind of key edges whose removal will disconnect a network and increase connected components. Here, we investigate the effects of removing bridges on controllability of network. Various strategies, including random deletion of edges, deletion based on betweenness centrality, and deletion based on degree of source or target nodes, are used to compare with the effect of removing bridges. It is found that the removing bridges strategy is more efficient on reducing controllability than the other strategies of removing edges for ER networks and scale-free networks. In addition, we also found the controllability robustness under edge attack is related to the average degree of complex networks. Therefore, we propose two optimization strategies based on bridges to improve the controllability robustness of complex networks against attacks. The effectiveness of the proposed strategies is demonstrated by simulation results of some model networks. These results are helpful for people to understand and control spreading processes of epidemic across different paths.

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.

Existence of chimera-like state in community structured networks

2020 ◽  
Vol 31 (05) ◽  
pp. 2050069 ◽  
Author(s):  
Nastaran Lotfi ◽  
Amir Hossein Darooneh
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Complex Network ◽  
Community Structures ◽  
Global Synchronization ◽  
Scale Free ◽  
Chimera State ◽  
Mixing Parameter ◽  
Scale Free Networks ◽  
Network Area

Synchronization is getting high attention in different fields specially in complex network area in the recent years. One of its new aspects is Chimera state in which some groups of oscillators are synchronized while the others are in the incoherent state. Here, we study how this state depends on the community structure in complex networks. In this work, we consider scale-free networks with community structures and study how the measurements such as the size of the community and mixing parameter could influence the global synchronization and chimera-like state.

scholarly journals Eigenvalue-based entropy in directed complex networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0251993
Author(s):  
Yan Sun ◽  
Haixing Zhao ◽  
Jing Liang ◽  
Xiujuan Ma
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Network Models ◽  
Small World ◽  
Laplacian Matrix ◽  
Directed Network ◽  
Small World Network ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

Entropy is an important index for describing the structure, function, and evolution of network. The existing research on entropy is primarily applied to undirected networks. Compared with an undirected network, a directed network involves a special asymmetric transfer. The research on the entropy of directed networks is very significant to effectively quantify the structural information of the whole network. Typical complex network models include nearest-neighbour coupling network, small-world network, scale-free network, and random network. These network models are abstracted as undirected graphs without considering the direction of node connection. For complex networks, modeling through the direction of network nodes is extremely challenging. In this paper, based on these typical models of complex network, a directed network model considering node connection in-direction is proposed, and the eigenvalue entropies of three matrices in the directed network is defined and studied, where the three matrices are adjacency matrix, in-degree Laplacian matrix and in-degree signless Laplacian matrix. The eigenvalue-based entropies of three matrices are calculated in directed nearest-neighbor coupling, directed small world, directed scale-free and directed random networks. Through the simulation experiment on the real directed network, the result shows that the eigenvalue entropy of the real directed network is between the eigenvalue entropy of directed scale-free network and directed small-world network.

scholarly journals A detailed characterization of complex networks using Information Theory

2021 ◽  
Author(s):  
CGS Freitas ◽  
ALL Aquino ◽  
HS Ramos ◽  
Alejandro Frery ◽  
OA Rosso
Keyword(s):  
Information Theory ◽  
Complex Networks ◽  
Complex Network ◽  
Preferential Attachment ◽  
Configuration Model ◽  
Complex Network Theory ◽  
Scale Free ◽  
Network Entropy ◽  
Law Degree

Understanding the structure and the dynamics of networks is of paramount importance for many scientific fields that rely on network science. Complex network theory provides a variety of features that help in the evaluation of network behavior. However, such analysis can be confusing and misleading as there are many intrinsic properties for each network metric. Alternatively, Information Theory methods have gained the spotlight because of their ability to create a quantitative and robust characterization of such networks. In this work, we use two Information Theory quantifiers, namely Network Entropy and Network Fisher Information Measure, to analyzing those networks. Our approach detects non-trivial characteristics of complex networks such as the transition present in the Watts-Strogatz model from k-ring to random graphs; the phase transition from a disconnected to an almost surely connected network when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-free networks when considering a non-linear preferential attachment, fitness, and aging features alongside the configuration model with a pure power-law degree distribution. Finally, we analyze the numerical results for real networks, contrasting our findings with traditional complex network methods. In conclusion, we present an efficient method that ignites the debate on network characterization.

scholarly journals A detailed characterization of complex networks using Information Theory

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Cristopher G. S. Freitas ◽  
Andre L. L. Aquino ◽  
Heitor S. Ramos ◽  
Alejandro C. Frery ◽  
Osvaldo A. Rosso
Keyword(s):  
Information Theory ◽  
Complex Networks ◽  
Complex Network ◽  
Preferential Attachment ◽  
Configuration Model ◽  
Complex Network Theory ◽  
Scale Free ◽  
Network Entropy ◽  
Law Degree

Abstract Understanding the structure and the dynamics of networks is of paramount importance for many scientific fields that rely on network science. Complex network theory provides a variety of features that help in the evaluation of network behavior. However, such analysis can be confusing and misleading as there are many intrinsic properties for each network metric. Alternatively, Information Theory methods have gained the spotlight because of their ability to create a quantitative and robust characterization of such networks. In this work, we use two Information Theory quantifiers, namely Network Entropy and Network Fisher Information Measure, to analyzing those networks. Our approach detects non-trivial characteristics of complex networks such as the transition present in the Watts-Strogatz model from k-ring to random graphs; the phase transition from a disconnected to an almost surely connected network when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-free networks when considering a non-linear preferential attachment, fitness, and aging features alongside the configuration model with a pure power-law degree distribution. Finally, we analyze the numerical results for real networks, contrasting our findings with traditional complex network methods. In conclusion, we present an efficient method that ignites the debate on network characterization.

scholarly journals A detailed characterization of complex networks using Information Theory

2021 ◽  
Author(s):  
CGS Freitas ◽  
ALL Aquino ◽  
HS Ramos ◽  
Alejandro Frery ◽  
OA Rosso
Keyword(s):  
Information Theory ◽  
Complex Networks ◽  
Complex Network ◽  
Preferential Attachment ◽  
Configuration Model ◽  
Complex Network Theory ◽  
Scale Free ◽  
Network Entropy ◽  
Law Degree

Understanding the structure and the dynamics of networks is of paramount importance for many scientific fields that rely on network science. Complex network theory provides a variety of features that help in the evaluation of network behavior. However, such analysis can be confusing and misleading as there are many intrinsic properties for each network metric. Alternatively, Information Theory methods have gained the spotlight because of their ability to create a quantitative and robust characterization of such networks. In this work, we use two Information Theory quantifiers, namely Network Entropy and Network Fisher Information Measure, to analyzing those networks. Our approach detects non-trivial characteristics of complex networks such as the transition present in the Watts-Strogatz model from k-ring to random graphs; the phase transition from a disconnected to an almost surely connected network when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-free networks when considering a non-linear preferential attachment, fitness, and aging features alongside the configuration model with a pure power-law degree distribution. Finally, we analyze the numerical results for real networks, contrasting our findings with traditional complex network methods. In conclusion, we present an efficient method that ignites the debate on network characterization.

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