Robustness and efficiency in interconnected networks with changes in network assortativity

Murakami, Masaya; Ishikura, Shu; Kominami, Daichi; Shimokawa, Tetsuya; Murata, Masayuki

doi:10.1007/s41109-017-0025-4

Cited by 30 publications

(31 citation statements)

References 24 publications

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“…First, we find that the GCS is increased when the intranetwork connectivity is assortative (i.e., η > 0) and the internetwork connectivity is disassortative (i.e., θ < 0). We have shown previously that an assortative single network is fragile against random failures [47]. However, it is notable that the results in Figure 7 tell us the opposite.…”

Section: Designing Influencers On the Pv-non Modelmentioning

confidence: 72%

“…Having constructed an initial VN topology with a specific degree distribution, we then set a target value of η and rewire the links continuously [47,48] until the assortativity η of the current topology approximates the given η target . Note that we rewire a topology so that it is not split into submodules; if the generated VN topology is separated into more than one module, the former is reconstructed so that it is fully connected.…”

Section: Intranetwork Assortativitymentioning

confidence: 99%

“…In a previous study [47], we developed a method for measuring the assortativity between networks to evaluate the interdependence of information networks. The assortativity of a set of links is represented as the sum of each link's contribution to the assortativity of the entire network.…”

Section: Internetwork Assortativitymentioning

confidence: 99%

See 2 more Smart Citations

Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks

Murakami

Kominami

Leibnitz³

et al. 2019

Applied Sciences

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In realizing the network environment assumed by the Internet-of-Things, network slicing has drawn considerable attention as a way to enhance the utilization of physical networks (PNs). Meanwhile, slicing has been shown to cause interdependence among sliced virtual networks (VNs) by propagating traffic fluctuations from one network to others. However, for interconnected networks with mutual dependencies, known as a network of networks (NoN), finding a reliable design method that can cope with environmental changes is an important issue that is yet to be addressed. Some NoN models exist that describe the behavior of interdependent networks in complex systems, and previous studies have shown that an NoN model based on the functional networks of the brain can achieve high robustness, but its application to dynamic and practical systems is yet to be considered. Consequently, this paper proposes the Physical–Virtual NoN (PV-NoN) model assuming a network-slicing environment. This model defines an NoN availability state to deal with traffic fluctuations and interdependence among a PN and VNs. Further, we assume three basic types of interdependence among VNs for this model. Simulation experiments confirm that the one applying complementary interdependence inspired by brain functional networks achieves high availability and communication performance while preventing interference among the VNs. Also investigated is a method for designing a reliable network structure for the PV-NoN model. To this end, the deployment of network influencers (i.e., the most influential elements over the entire network) is configured from the perspective of intra/internetwork assortativity. Simulation experiments confirm that availability or communication performance is improved when each VN is formed assortatively or disassortatively, respectively. Regarding internetwork assortativity, both the availability and communication performance are improved when the influencers are deployed disassortatively among the VNs.

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Section: Designing Influencers On the Pv-non Modelmentioning

confidence: 72%

Section: Intranetwork Assortativitymentioning

confidence: 99%

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Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks

Murakami

Kominami

Leibnitz³

et al. 2019

Applied Sciences

Self Cite

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“…As for the brain network, which is composed of multiple modules, assortativity can indicate different behaviors depending on whether the focus is on the connections between the modules or within the modules as well as depending on whether the connections between the modules are strong or weak. High reliability of brain networks can be attributed to the topological connectivity, which is based on the assortativity between and within network modules [ 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks

Murakami

Kominami

Leibnitz³

et al. 2018

Sensors

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Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.

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“…The existence of a degree-degree correlation between nodes is an important property of networks. It has been reported that different correlations arise in different kinds of networks brain networks tend to be assortative SPORNS, 2012;MURAKAMI et al, 2017). By other hand technological and biological systems have disassortative structures.…”

Section: Assortativity Networkmentioning

confidence: 99%

Self-organization map in complex network

Pimenta¹

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This work is dedicated to God for allowing me to fulfill a desire of my heart and is also dedicated to my husband. Thank you for your unconditional support. I would like to thank Prof. Francisco Rodrigues for supervising this Ph.D. and for introducing me to the world of complex networks. I am very grateful to Prof. Luciano da Fontoura Costa for giving me the opportunity to work and learn from him. I am also very grateful to Prof. Cesar Comin for his pertinent comments, suggestions about the work developed in this thesis.

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Robustness and efficiency in interconnected networks with changes in network assortativity

Cited by 30 publications

References 24 publications

Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks

Reliable Design for a Network of Networks with Inspiration from Brain Functional Networks

Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks

Self-organization map in complex network

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