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The Internet is rapidly developing toward the next generation of the Internet of Things (IoT), which accelerates the emergence of interconnected network architectures even further. However, the way to design interconnected network...
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The Internet is rapidly developing toward the next generation of the Internet of Things (IoT), which accelerates the emergence of interconnected network architectures even further. However, the way to design interconnected networks that can meet various changes in environment and service demands remains an important issue that has not been addressed yet. The interconnected networks should suppress or prevent diffusion of malicious information, whereas they should enhance diffuse urgent information around the whole networks. In this study, we propose an Network of Networks (NoN) model inspired by the nature of modular interconnected networks in the brain. Our proposed NoN model can prevent malicious information to diffuse one subnetwork to another, but not that takes place on interconnecting links. In order to find a strategy to change the speed of information diffusion, we further configure the connectivity within and between subnetworks of the interconnected networks that matches our proposing model. Through simulation experiments, we confirmed that our proposing model can diffuse information as fast as a purely interconnected networks, that prevent no information on the interconnecting links. The results also show that our proposed model reduce the speed of the information diffusion almost the same as that of the worst case in a independent subnetwork, that has no interconnecting links.
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摘要 :
The Internet is rapidly developing toward the next generation of the Internet of Things (IoT), which accelerates the emergence of interconnected network architectures even further. In the context of IoT, not only the Internet serv...
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The Internet is rapidly developing toward the next generation of the Internet of Things (IoT), which accelerates the emergence of interconnected network architectures even further. In the context of IoT, not only the Internet services becomes sophisticated and diversified, the existing social infrastructure (e.g., transportation, electricity) are expected to be connected and dependent on the Internet. It is pointed that due to the inter-dependency between networks, a partial malfunction in a network can propagate to other interconnected networks. In this study, as an inspiration to solve this issue, we focus on a method to detect influencers in an inter-dependent networks of the human brain. By configuring connectivity patterns between subnetworks of an interconnected network, where inter-modular dependency exist, our evaluation shows the feasibility of centralization and distribution of influencers regarding its influence and deployment. At the same time, our results showed that the deterioration of the robustness caused by inter-dependency can be reduced as that of interconnected networks where no inter-modular dependency exists.
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Robustness of network of networks (NON) has been studied only for dependency coupling (Gao et al., 2012) and only for connectivity coupling (Leicht and Souza, arxiv:0907.0894). The case of network of networks with both interdepend...
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Robustness of network of networks (NON) has been studied only for dependency coupling (Gao et al., 2012) and only for connectivity coupling (Leicht and Souza, arxiv:0907.0894). The case of network of networks with both interdependent and interconnected links is more complicated, and also more close to real-life coupled network systems. Understanding the robustness of NON with interdependent and interconnected links is helpful to design resilient infrastructures. Here we develop a framework to study analytically and numerically the robustness of this system with no-feedback and feedback conditions for the case of starlike NON. When assumed that all degree distributions of the connectivity intra- and inter-links are Poissonian, we find that the system undergoes from second order through hybrid order to first order phase transition as coupling strength q increases. Additionally, for both conditions, the results suggest that increasing density of connectivity links (intra-connectivity links or inter-connectivity links) can increase the robustness of the system, while the interdependency links decrease its robustness. Furthermore, by comparing critical attacking strength under same parameters for both conditions, we also find that feedback condition of dependency links, in contrast to no-feedback condition, makes the system extremely vulnerable. Although our detailed analysis is for Poisson degree distribution, the theory can be applied to any degree distribution and other basic topological structure of network such like tree-like and loop-like NON. (C) 2015 Elsevier B.V. All rights reserved.
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This study explores the evolving structure of the rising field of "network of networks" (NoN). Reviewing publications dating back to 1931, we describe the evolution of major NoN research themes in different scientific disciplines ...
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This study explores the evolving structure of the rising field of "network of networks" (NoN). Reviewing publications dating back to 1931, we describe the evolution of major NoN research themes in different scientific disciplines and the gradual emergence of an integrated field. We analyse the co-occurrence networks of keywords used in all 7818 scientific publications in Scopus database that mention NoN and other related terms (i.e., "interconnected networks", "multilayer networks", "multiplex networks", "interdependent networks", "multinetworks", "multilevel networks", and "multidimensional networks"). The results show that the NoN began to form as a field mainly in the 1990s around research on neural networks. Diverse aspects of NoN research, indicated by dominant keywords such as "interconnection", "multilayer", and "interdependence", gradually spread to computer and physical sciences. As of 2018, network interdependence - with its application in network resilience and prevention of cascading failure - seems to be one of the key topics attracting broad academic attention. Another noteworthy observation is the emergence of a distinct cluster of terms relevant to nanoscience and nanotechnology. It is envisaged from the analysis that NoN concepts will develop stronger ties with nanoscience with increasing understanding and data acquisition from the molecular, atomic, and subatomic levels. (C) 2021 Published by Elsevier B.V.
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It seems a universal phenomenon of networks that the attacks on a small number of nodes by an adversary player Alice may generate a global cascading failure of the networks. It has been shown (Li et al., 2013) that classic scale-f...
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It seems a universal phenomenon of networks that the attacks on a small number of nodes by an adversary player Alice may generate a global cascading failure of the networks. It has been shown (Li et al., 2013) that classic scale-free networks (Barabasi and Albert, 1999, Barabasi, 2009) are insecure against attacks of as small as 0(log n) many nodes. This poses a natural and fundamental question: Can we introduce a second player Bob to prevent Alice from global cascading failure of the networks? We proposed a game in networks. We say that a network has an equilibrium game if the second player Bob has a strategy to balance the cascading influence of attacks by the adversary player Alice. It was shown that networks of the preferential attachment model (Barabasi and Albert, 1999) fail to have equilibrium games, that random graphs of the Erdos-Renyi model (Erdos and Renyi, 1959, Eras and Renyi, 1960) have, for which randomness is the mechanism, and that homophyly networks (Li et al., 2013) have equilibrium games, for which homophyly and preferential attachment are the underlying mechanisms. We found that some real networks have equilibrium games, but most real networks fail to have. We anticipate that our results lead to an interesting new direction of network theory, that is, equilibrium games in networks. (C) 2014 Elsevier B.V. All rights reserved.
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This article reviews and discusses the empirical literature on interorganizational networks at the network level of analysis, or what is sometimes referred to as "whole" networks. An overview of the distinction between egocentric ...
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This article reviews and discusses the empirical literature on interorganizational networks at the network level of analysis, or what is sometimes referred to as "whole" networks. An overview of the distinction between egocentric and network-level research is first introduced. Then, a review of the modest literature on whole networks is undertaken, along with a summary table outlining the main findings based on a thorough literature search. Finally, the authors offer a discussion concerning what future directions might be taken by researchers hoping to expand this important, but understudied, topic.
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We investigate the defending of networks against virus attack. We define the resistance of a network to be the maximum number of bits required to determine the code of the module that is accessible from random walk, from which ran...
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We investigate the defending of networks against virus attack. We define the resistance of a network to be the maximum number of bits required to determine the code of the module that is accessible from random walk, from which random walk cannot escape. We show that for any network G, R(G) = H-1 (G) - H-2 (G), where R(G) is the resistance of G, H-1 (G) and H-2(G) are the one-and two-dimensional structural information of G, respectively, and that resistance maximization is the principle for defending networks against virus attack. By using the theory, we investigate the defending of real world networks and of the networks generated by the preferential attachment and the security models. We show that there exist networks that are defensible by a small number of controllers from cascading failure of any virus attack. Our theory demonstrates that resistance maximization is the principle for defending networks against virus attacks. (C) 2016 Elsevier B.V. All rights reserved.
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Many complex networks have recently been recognized to involve significant interdependence between different systems. Motivation comes primarily from infrastructures like power grids and communications networks, but also includes ...
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Many complex networks have recently been recognized to involve significant interdependence between different systems. Motivation comes primarily from infrastructures like power grids and communications networks, but also includes areas such as the human brain and finance. Interdependence implies that when components in one system fail, they lead to failures in the same system or other systems. This can then lead to additional failures finally resulting in a long cascade that can cripple the entire system. Furthermore, many of these networks, in particular infrastructure networks, are embedded in space and thus have unique spatial properties that significantly decrease their resilience to failures. Here we present a review of novel results on interdependent spatial networks and how cascading processes are affected by spatial embedding. We include various aspects of spatial embedding such as cases where dependencies are spatially restricted and localized attacks on nodes contained in some spatial region of the network. In general, we find that spatial networks are more vulnerable when they are interdependent and that they are more likely to undergo abrupt failure transitions than interdependent non-embedded networks. We also present results on recovery in spatial networks, the nature of cascades due to overload failures in these networks, and some examples of percolation features found in real-world tra?c networks. Finally, we conclude with an outlook on future possible research directions in this area.
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Using numerical simulations, we investigate the effects of the connectivity and topologies of network on the quality of transport between connected scale free networks. Hence, the flow as the electrical conductance between connect...
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Using numerical simulations, we investigate the effects of the connectivity and topologies of network on the quality of transport between connected scale free networks. Hence, the flow as the electrical conductance between connected networks is calculated. It is found that the conductance distribution between networks follow a power law psi(C) similar to C(2 lambda-1) where lambda is the exponent of the global Network of network, we show that the transport in the symmetric growing preferential attachment connection is more efficient than the symmetric static preferential attachment connection. Furthermore, the differences of transport and networks communications properties in the different cases are discussed.
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Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks chan...
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Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.
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