The Experts below are selected from a list of 372654 Experts worldwide ranked by ideXlab platform
Shlomo Havlin - One of the best experts on this subject based on the ideXlab platform.
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percolation of localized attack on Complex Networks
New Journal of Physics, 2015Co-Authors: Shuai Shao, Shlomo Havlin, Xuqing Huang, Eugene H StanleyAbstract:The robustness of Complex Networks against node failure and malicious attack has been of interest for decades, while most of the research has focused on random attack or hub-targeted attack. In many real-world scenarios, however, attacks are neither random nor hub-targeted, but localized, where a group of neighboring nodes in a network are attacked and fail. In this paper we develop a percolation framework to analytically and numerically study the robustness of Complex Networks against such localized attack. In particular, we investigate this robustness in Erd?s?R?nyi Networks, random-regular Networks, and scale-free Networks. Our results provide insight into how to better protect Networks, enhance cybersecurity, and facilitate the design of more robust infrastructures.
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percolation of localized attack on Complex Networks
arXiv: Physics and Society, 2014Co-Authors: Shuai Shao, Shlomo Havlin, Xuqing Huang, Eugene H StanleyAbstract:The robustness of Complex Networks against node failure and malicious attack has been of interest for decades, while most of the research has focused on random attack or hub-targeted attack. In many real-world scenarios, however, attacks are neither random nor hub-targeted, but localized, where a group of neighboring nodes in a network are attacked and fail. In this paper we develop a percolation framework to analytically and numerically study the robustness of Complex Networks against such localized attack. In particular, we investigate this robustness in Erd\H{o}s-R\'{e}nyi Networks, random-regular Networks, and scale-free Networks. Our results provide insight into how to better protect Networks, enhance cybersecurity, and facilitate the design of more robust infrastructures.
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identification of influential spreaders in Complex Networks
Nature Physics, 2010Co-Authors: Shlomo Havlin, Maksim Kitsak, Eugene H Stanley, Lazaros K Gallos, Fredrik Liljeros, Lev Muchnik, Hernan A MakseAbstract:Spreading of information, ideas or diseases can be conveniently modelled in the context of Complex Networks. An analysis now reveals that the most efficient spreaders are not always necessarily the most connected agents in a network. Instead, the position of an agent relative to the hierarchical topological organization of the network might be as important as its connectivity.
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Complex Networks structure robustness and function
2010Co-Authors: Reuven Cohen, Shlomo HavlinAbstract:1. Introduction Part I. Random Network Models: 2. The Erdos-Renyi models 3. Observations in real-world Networks 4. Models for Complex Networks 5. Growing network models Part II. Structure and Robustness of Complex Networks: 6. Distances in scale-free Networks - the ultra small world 7. Self-similarity in Complex Networks 8. Distances in geographically embedded Networks 9. The network's structure - the generating function method 10. Percolation on Complex Networks 11. Structure of random directed Networks - the bow tie 12. Introducing weights - bandwidth allocation and multimedia broadcasting Part III. Network Function - Dynamics and Applications: 13. Optimization of the network structure 14. Epidemiological models 15. Immunization 16. Thermodynamic models on Networks 17. Spectral properties, transport, diffusion and dynamics 18. Searching in Networks 19. Biological Networks and network motifs Part IV. Appendices References Index.
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Complex Networks structure robustness and function
2010Co-Authors: Reuven Cohen, Shlomo HavlinAbstract:1. Introduction Part I. Random Network Models: 2. The Erdos-Renyi models 3. Observations in real-world Networks 4. Models for Complex Networks 5. Growing network models Part II. Structure and Robustness of Complex Networks: 6. Distances in scale-free Networks - the ultra small world 7. Self-similarity in Complex Networks 8. Distances in geographically embedded Networks 9. The network's structure - the generating function method 10. Percolation on Complex Networks 11. Structure of random directed Networks - the bow tie 12. Introducing weights - bandwidth allocation and multimedia broadcasting Part III. Network Function - Dynamics and Applications: 13. Optimization of the network structure 14. Epidemiological models 15. Immunization 16. Thermodynamic models on Networks 17. Spectral properties, transport, diffusion and dynamics 18. Searching in Networks 19. Biological Networks and network motifs Part IV. Appendices References Index.
Zhongyuan Zhang - One of the best experts on this subject based on the ideXlab platform.
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enhanced community structure detection in Complex Networks with partial background information
Scientific Reports, 2013Co-Authors: Zhongyuan Zhang, Kaidi Sun, Siqi WangAbstract:Enhanced Community Structure Detection in Complex Networks with Partial Background Information
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community structure detection in Complex Networks with partial background information
EPL, 2013Co-Authors: Zhongyuan ZhangAbstract:Constrained clustering has been well-studied in the unsupervised learning society. However, how to encode constraints into community structure detection, within Complex Networks, remains a challenging problem. In this paper, we propose a semi-supervised learning framework for community structure detection. This framework implicitly encodes the must-link and cannot-link constraints by modifying the adjacency matrix of network, which can also be regarded as de-noising the consensus matrix of community structures. Our proposed method gives consideration to both the topology and the functions (background information) of Complex network, which enhances the interpretability of the results. The comparisons performed on both the synthetic benchmarks and the real-world Networks show that the proposed framework can significantly improve the community detection performance with few constraints, which makes it an attractive methodology in the analysis of Complex Networks.
Yong Deng - One of the best experts on this subject based on the ideXlab platform.
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measure the structure similarity of nodes in Complex Networks based on relative entropy
Physica A-statistical Mechanics and Its Applications, 2018Co-Authors: Qi Zhang, Yong DengAbstract:Abstract Similarity of nodes is a basic structure quantification in Complex Networks. Lots of methods in research on Complex Networks are based on nodes’ similarity such as node’s classification, network’s community structure detection, network’s link prediction and so on. Therefore, how to measure nodes’ similarity is an important problem in Complex Networks. In this paper, a new method is proposed to measure nodes’ structure similarity based on relative entropy and each node’s local structure. In the new method, each node’s structure feature can be quantified as a special kind of information. The quantification of similarity between different pair of nodes can be replaced as the quantification of similarity in structural information. Then relative entropy is used to measure the difference between each pair of nodes’ structural information. At last the value of relative entropy between each pair of nodes is used to measure nodes’ structure similarity in Complex Networks. Comparing with existing methods the new method is more accuracy to measure nodes’ structure similarity.
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modeling the self similarity in Complex Networks based on coulomb s law
Communications in Nonlinear Science and Numerical Simulation, 2016Co-Authors: Yong Deng, Haixin Zhang, Daijun Wei, Xin LanAbstract:Abstract Recently, self-similarity of Complex Networks have attracted much attention. Fractal dimension of Complex network is an open issue. Hub repulsion plays an important role in fractal topologies. This paper models the repulsion among the nodes in the Complex Networks in calculation of the fractal dimension of the Networks. Coulomb’s law is adopted to represent the repulse between two nodes of the network quantitatively. A new method to calculate the fractal dimension of Complex Networks is proposed. The Sierpinski triangle network and some real Complex Networks are investigated. The results are illustrated to show that the new model of self-similarity of Complex Networks is reasonable and efficient.
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weighted k shell decomposition for Complex Networks based on potential edge weights
Physica A-statistical Mechanics and Its Applications, 2015Co-Authors: Bo Wei, Yong Deng, Daijun Wei, Jie Liu, Cai GaoAbstract:Identifying influential nodes in Complex Networks has attracted much attention because of its great theoretical significance and wide application. Existing methods consider the edges equally when designing identifying methods for the unweighted Networks. In this paper, we propose an edge weighting method based on adding the degree of its two end nodes and for the constructed weighted Networks, we propose a weighted k-shell decomposition method (Wks). Further investigations on the epidemic spreading process of the Susceptible–Infected–Recovered (SIR) model and Susceptible–Infected (SI) model in real Complex Networks verify that our method is effective for detecting the node influence.
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Local structure entropy of Complex Networks.
arXiv: Social and Information Networks, 2014Co-Authors: Qi Zhang, Yong DengAbstract:Identifying influential nodes in the Complex Networks is of theoretical and practical significance. There are many methods are proposed to identify the influential nodes in the Complex Networks. In this paper, a local structure entropy which is based on the degree centrality and the statistical mechanics is proposed to identifying the influential nodes in the Complex network. In the definition of the local structure entropy, each node has a local network, the local structure entropy of each node is equal to the structure entropy of the local network. The main idea in the local structure entropy is try to use the influence of the local network to replace the node's influence on the whole network. The influential nodes which are identified by the local structure entropy are the intermediate nodes in the network. The intermediate nodes which connect those nodes with a big value of degree. We use the $Susceptible-Infective$ (SI) model to evaluate the performance of the influential nodes which are identified by the local structure entropy. In the SI model the nodes use as the source of infection. According to the SI model, the bigger the percentage of the infective nodes in the network the important the node to the whole Networks. The simulation on four real Networks show that the proposed method is efficacious and rationality to identify the influential nodes in the Complex Networks.
Siqi Wang - One of the best experts on this subject based on the ideXlab platform.
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enhanced community structure detection in Complex Networks with partial background information
Scientific Reports, 2013Co-Authors: Zhongyuan Zhang, Kaidi Sun, Siqi WangAbstract:Enhanced Community Structure Detection in Complex Networks with Partial Background Information
Albertlaszlo Barabasi - One of the best experts on this subject based on the ideXlab platform.
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control principles of Complex Networks
Reviews of Modern Physics, 2016Co-Authors: Yangyu Liu, Albertlaszlo BarabasiAbstract:A reflection of our ultimate understanding of a Complex system is our ability to control its behavior. Typically, control has multiple prerequisites: It requires an accurate map of the network that governs the interactions between the system's components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in nonlinear dynamics and control theory, notions of control and controllability have taken a new life recently in the study of Complex Networks, inspiring several fundamental questions: What are the control principles of Complex systems? How do Networks organize themselves to balance control with functionality? To address these here we review recent advances on the controllability and the control of Complex Networks, exploring the intricate interplay between a system's structure, captured by its network topology, and the dynamical laws that govern the interactions between the components. We match the pertinent mathematical results with empirical findings and applications. We show that uncovering the control principles of Complex systems can help us explore and ultimately understand the fundamental laws that govern their behavior.
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target control of Complex Networks
Nature Communications, 2014Co-Authors: Jianxi Gao, Albertlaszlo Barabasi, Yangyu Liu, Raissa M DsouzaAbstract:Network controllability has numerous applications in natural and technological systems. Here, Gao et al. develop a theoretical approach and a greedy algorithm to study target control—the ability to efficiently control a preselected subset of nodes—in Complex Networks.
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scale free and hierarchical structures in Complex Networks
MODELING OF COMPLEX SYSTEMS: Seventh Granada Lectures, 2003Co-Authors: Albertlaszlo Barabasi, Zoltan Dezső, Erzsebet Ravasz, Soonhyung Yook, Zoltan N OltvaiAbstract:Networks with Complex topology describe systems as diverse as the cell or the World Wide Web. The emergence of these Networks is driven by self-organizing processes that are governed by simple but generic laws. In the last three years it became clear that many Complex Networks, such as the Internet, the cell, or the world wide web, share the same large-scale topology. Here we review recent advances in the characterization of Complex Networks, focusing the emergence of the scale-free and the hierarchical architecture. We also present empirical results to demonstrate that the scale-free and the hierarchical property are shared by a wide range of Complex Networks. Finally, we discuss the impact of the network topology on our ability to stop the spread of viruses in Complex Networks.
