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Kusunoki R, Hayashi Y. Investigating stronger tolerant network against cascading failures in focusing on changing degree distributions. PLoS One 2024; 19:e0297094. [PMID: 38985814 PMCID: PMC11236162 DOI: 10.1371/journal.pone.0297094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 12/22/2023] [Indexed: 07/12/2024] Open
Abstract
Many real-world networks with Scale-Free structure are significantly vulnerable against both intentional attacks and catastrophic cascading failures. On the other hand, it has been shown that networks with narrower degree distributions have strong robustness of connectivity by enhancing loops. This paper numerically reveals that such networks are also tolerant against cascading failures. Our findings will be useful in designing stronger tolerant network infrastructures.
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Affiliation(s)
- Ryota Kusunoki
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
| | - Yukio Hayashi
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
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2
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Lou Y, Wu R, Li J, Wang L, Li X, Chen G. A Learning Convolutional Neural Network Approach for Network Robustness Prediction. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:4531-4544. [PMID: 36215351 DOI: 10.1109/tcyb.2022.3207878] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Network robustness is critical for various societal and industrial networks against malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible for large-scale networks. In this article, an improved method for network robustness prediction is developed based on learning feature representation using the convolutional neural network (LFR-CNN). In this scheme, the higher-dimensional network data are compressed into lower-dimensional representations, which are then passed to a convolutional neural network to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that: 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly smaller prediction errors; 2) LFR-CNN is insensitive to the variation of the input network size, which significantly extends its applicability; 3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations; and 4) LFR-CNN not only accurately predicts the network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures.
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3
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Lou Y, Wu R, Li J, Wang L, Tang CB, Chen G. Classification-based prediction of network connectivity robustness. Neural Netw 2023; 157:136-146. [DOI: 10.1016/j.neunet.2022.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 08/29/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022]
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4
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Peng Y, Liu C, Wu Y, Liu S, Wang K. Graph convolutional networks-based robustness optimization for scale-free Internet of Things. INTELL DATA ANAL 2022. [DOI: 10.3233/ida-216222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The Internet of Things (IoT) devices have limited resources and are vulnerable to attacks, so optimizing their network topology to resist random failures and malicious attacks has become a key issue. The scale-free network model has strong resistance to random attacks, but it is very vulnerable to malicious attacks. The existing studies mostly adopt heuristic algorithms to optimize the ability of scale-free networks to resist malicious attacks, but their high computational cost cannot meet the timeliness requirements of the real IoT. Therefore, this paper proposes an intelligent topology robustness optimization model based on a graph convolutional network (ROGCN). The model extracts the onion-like structural features of the highly robust network topology from the data set through supervised learning, and on this basis, different search strategies are designed to meet the needs of different IoT scenarios. The extensive experimental results demonstrate that ROGCN can more effectively improve the robustness of scale-free IoT networks against malicious attacks compared to two existing heuristic algorithms, with a lower computational cost.
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5
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Chujyo M, Hayashi Y. Adding links on minimum degree and longest distance strategies for improving network robustness and efficiency. PLoS One 2022; 17:e0276733. [PMID: 36288333 PMCID: PMC9605036 DOI: 10.1371/journal.pone.0276733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022] Open
Abstract
Many real-world networks characterized by power-law degree distributions are extremely vulnerable against malicious attacks. Therefore, it is important to obtain effective methods for strengthening the robustness of the existing networks. Previous studies have been discussed some link addition methods for improving the robustness. In particular, two effective strategies for selecting nodes to add links have been proposed: the minimum degree and longest distance strategies. However, it is unclear whether the effects of these strategies on the robustness are independent or not. In this paper, we investigate the contributions of these strategies to improving the robustness by adding links in distinguishing the effects of degrees and distances as much as possible. Through numerical simulation, we find that the robustness is effectively improved by adding links on the minimum degree strategy for both synthetic trees and real networks. As an exception, only when the number of added links is small, the longest distance strategy is the best. Conversely, the robustness is only slightly improved by adding links on the shortest distance strategy in many cases, even combined with the minimum degree strategy. Therefore, enhancing global loops is essential for improving the robustness rather than local loops.
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Affiliation(s)
- Masaki Chujyo
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
- * E-mail:
| | - Yukio Hayashi
- Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
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6
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Lou Y, He Y, Wang L, Tsang KF, Chen G. Knowledge-Based Prediction of Network Controllability Robustness. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:5739-5750. [PMID: 33861714 DOI: 10.1109/tnnls.2021.3071367] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Network controllability robustness (CR) reflects how well a networked system can maintain its controllability against destructive attacks. Its measure is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the CR is determined by attack simulations, which is computationally time-consuming or even infeasible. In this article, an improved method for predicting the network CR is developed based on machine learning using a group of convolutional neural networks (CNNs). In this scheme, a number of training data generated by simulations are used to train the group of CNNs for classification and prediction, respectively. Extensive experimental studies are carried out, which demonstrate that 1) the proposed method predicts more precisely than the classical single-CNN predictor; 2) the proposed CNN-based predictor provides a better predictive measure than the traditional spectral measures and network heterogeneity.
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7
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Kojima R, Legaspi R, Murofushi T. Fuzzy Clustering in Assortative and Disassortative Networks. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2021. [DOI: 10.20965/jaciii.2021.p0989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Despite the significance of assortativity as a property of networks that paves for the emergence of new structural types, surprisingly, there has been little research done on assortativity. Assortative networks are perhaps among the most prominent examples of complex networks believed to be governed by common phenomena, thereby producing structures far from random. Further, certain vertices possess high centrality and can be regarded as significant and influential vertices that can become cluster centers that connect with high membership to many of the surrounding vertices. We propose a fuzzy clustering method to meaningfully characterize assortative, as well as disassortative, networks by adapting the Bonacichi’s power centrality to seek the high degree centrality vertices to become cluster centers. Moreover, we leverage our novel modularity function to determine the optimal number of clusters, as well as the optimal membership among clusters. However, due to the difficulty of finding real-world assortative network datasets that come with ground truths, we evaluated our method using synthetic data but possibly bearing resemblance to real-world network datasets as they were generated by the Lancichinetti–Fortunato–Radicchi benchmark. Our results show our non-hierarchical method outperforms a known hierarchical fuzzy clustering method, and also performs better than a well-known membership-based modularity function. Our method proved to perform beyond satisfactory for both assortative and disassortative networks.
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8
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Sugishita K, Abdel-Mottaleb N, Zhang Q, Masuda N. A growth model for water distribution networks with loops. Proc Math Phys Eng Sci 2021; 477:20210528. [PMID: 35153598 PMCID: PMC8610702 DOI: 10.1098/rspa.2021.0528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/26/2021] [Indexed: 11/12/2022] Open
Abstract
Water distribution networks (WDNs) expand their service areas over time. These growth dynamics are poorly understood. One facet of WDNs is that they have loops in general, and closing loops may be a functionally important process for enhancing their robustness and efficiency. We propose a growth model for WDNs that generates networks with loops and is applicable to networks with multiple water sources. We apply the proposed model to four empirical WDNs to show that it produces networks whose structure is similar to that of the empirical WDNs. The comparison between the empirical and modelled WDNs suggests that the empirical WDNs may realize a reasonable balance between cost, efficiency and robustness in terms of the network structure. We also study the design of pipe diameters based on a biological positive feedback mechanism. Specifically, we apply a model inspired by Physarum polycephalum to find moderate positive correlations between the empirical and modelled pipe diameters. The difference between the empirical and modelled pipe diameters suggests that we may be able to improve the performance of WDNs by following organizing principles of biological flow networks.
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Affiliation(s)
- Kashin Sugishita
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY 14260-2900, USA
- Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, 152-8550 Tokyo, Japan
| | - Noha Abdel-Mottaleb
- Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, USA
| | - Qiong Zhang
- Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, USA
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, NY 14260-2900, USA
- Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, Buffalo, NY 14260-5030, USA
- Faculty of Science and Engineering, Waseda University, 169-8555 Tokyo, Japan
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9
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More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource. ENTROPY 2021; 23:e23010102. [PMID: 33445680 PMCID: PMC7828154 DOI: 10.3390/e23010102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 01/07/2021] [Accepted: 01/07/2021] [Indexed: 11/17/2022]
Abstract
Complex network infrastructure systems for power supply, communication, and transportation support our economic and social activities; however, they are extremely vulnerable to frequently increasing large disasters or attacks. Thus, the reconstruction of a damaged network is more advisable than an empirically performed recovery of the original vulnerable one. To reconstruct a sustainable network, we focus on enhancing loops so that they are not trees, which is made possible by node removal. Although this optimization corresponds with an intractable combinatorial problem, we propose self-healing methods based on enhancing loops when applying an approximate calculation inspired by statistical physics. We show that both higher robustness and efficiency are obtained in our proposed methods by saving the resources of links and ports when compared to ones in conventional healing methods. Moreover, the reconstructed network can become more tolerant than the original when some damaged links are reusable or compensated for as an investment of resource. These results present the potential of network reconstruction using self-healing with adaptive capacity in terms of resilience.
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10
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Sasaki H, Sakata I. Business partner selection considering supply-chain centralities and causalities. SUPPLY CHAIN FORUM 2021. [DOI: 10.1080/16258312.2020.1824531] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Hajime Sasaki
- Institute for Future Initiatives, The University of Tokyo, Tokyo, Japan
| | - Ichiro Sakata
- Department of Technology Management for Innovation, The University of Tokyo, Tokyo, Japan
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11
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Abstract
The threat of a malicious attack is one of the major security problems in complex networks. Resilience is the system-level self-adjusting ability of a complex network to retain its basic functionality and recover rapidly from major disruptions. Despite numerous heuristic enhancement methods, there is a research gap in maximizing network resilience: current heuristic methods are designed to immunize vital nodes or modify a network to a specific onion-like structure and cannot maximize resilience theoretically via network structure. Here we map complex networks onto a physical elastic system to introduce indices of network resilience, and propose a unified theoretical framework and general approach, which can address the optimal problem of network resilience by slightly modifying network structures (i.e., by adding a set of structural edges). We demonstrate the high efficiency of this approach on three realistic networks as well as two artificial random networks. Case studies show that the proposed approach can maximize the resilience of complex networks while maintaining their topological functionality. This approach helps to unveil hitherto hidden functions of some inconspicuous components, which in turn, can be used to guide the design of resilient systems, offer an effective and efficient approach for mitigating malicious attacks, and furnish self-healing to reconstruct failed infrastructure systems.
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12
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Hayashi Y, Uchiyama N. Onion-like networks are both robust and resilient. Sci Rep 2018; 8:11241. [PMID: 30050045 PMCID: PMC6062544 DOI: 10.1038/s41598-018-29626-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 07/16/2018] [Indexed: 11/12/2022] Open
Abstract
Tolerant connectivity and flow transmission within capacity are crucial functions as network. However, the threats to malicious attacks based on intelligent node selections and rapid breakdown by cascading overload failures increase more and more with large blackout or congestion in our contemporary networking systems and societies. It has been recently suggested that interwoven loops protect the network functions from such damages, but it is a computationally intractable combinatorial problem to maximize a set of necessary nodes for loops in order to improve the robustness. We propose a new method by enhancing loops in the incremental growth for constructing onion-like networks with positive degree-degree correlations, whose topological structure has the optimal tolerance of connectivity against attacks in the state-of-the-art. Moreover, we find out that onion-like networks acquire adaptive capacity in resilience by a change of routing policy for flow control to absorb cascading overload failures triggered by a single attack and simultaneous multi-attacks. The inhibitory effect is stronger than that in scale-free networks found in many real systems.
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Affiliation(s)
- Yukio Hayashi
- Japan Advanced Institute of Science and Technology, Graduate School of Advanced Institute of Science and Technology/Division of Transdisiplinary Sciences, Ishikawa, 923-1292, Japan.
| | - Naoya Uchiyama
- Japan Advanced Institute of Science and Technology, Graduate School of Advanced Institute of Science and Technology/Division of Transdisiplinary Sciences, Ishikawa, 923-1292, Japan
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13
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Fujiki Y, Takaguchi T, Yakubo K. General formulation of long-range degree correlations in complex networks. Phys Rev E 2018; 97:062308. [PMID: 30011590 DOI: 10.1103/physreve.97.062308] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Indexed: 11/07/2022]
Abstract
We provide a general framework for analyzing degree correlations between nodes separated by more than one step (i.e., beyond nearest neighbors) in complex networks. One joint and four conditional probability distributions are introduced to fully describe long-range degree correlations with respect to degrees k and k^{'} of two nodes and shortest path length l between them. We present general relations among these probability distributions and clarify the relevance to nearest-neighbor degree correlations. Unlike nearest-neighbor correlations, some of these probability distributions are meaningful only in finite-size networks. Furthermore, as a baseline to determine the existence of intrinsic long-range degree correlations in a network other than inevitable correlations caused by the finite-size effect, the functional forms of these probability distributions for random networks are analytically evaluated within a mean-field approximation. The utility of our argument is demonstrated by applying it to real-world networks.
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Affiliation(s)
- Yuka Fujiki
- Department of Applied Physics, Hokkaido University, Sapporo 060-8628, Japan
| | - Taro Takaguchi
- National Institute of Information and Communications Technology, Tokyo 184-8795, Japan
| | - Kousuke Yakubo
- Department of Applied Physics, Hokkaido University, Sapporo 060-8628, Japan
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14
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Di Muro MA, Valdez LD, Aragão Rêgo HH, Buldyrev SV, Stanley HE, Braunstein LA. Cascading Failures in Interdependent Networks with Multiple Supply-Demand Links and Functionality Thresholds. Sci Rep 2017; 7:15059. [PMID: 29118418 PMCID: PMC5678122 DOI: 10.1038/s41598-017-14384-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Accepted: 10/03/2017] [Indexed: 11/09/2022] Open
Abstract
Various social, financial, biological and technological systems can be modeled by interdependent networks. It has been assumed that in order to remain functional, nodes in one network must receive the support from nodes belonging to different networks. So far these models have been limited to the case in which the failure propagates across networks only if the nodes lose all their supply nodes. In this paper we develop a more realistic model for two interdependent networks in which each node has its own supply threshold, i.e., they need the support of a minimum number of supply nodes to remain functional. In addition, we analyze different conditions of internal node failure due to disconnection from nodes within its own network. We show that several local internal failure conditions lead to similar nontrivial results. When there are no internal failures the model is equivalent to a bipartite system, which can be useful to model a financial market. We explore the rich behaviors of these models that include discontinuous and continuous phase transitions. Using the generating functions formalism, we analytically solve all the models in the limit of infinitely large networks and find an excellent agreement with the stochastic simulations.
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Affiliation(s)
- M A Di Muro
- Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR)-Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata-CONICET, Funes, 3350, (7600) Mar del Plata, Argentina.
| | - L D Valdez
- Instituto de Física Enrique Gaviola, CONICET, Ciudad Universitaria, 5000, Córdoba, Argentina
- Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, 5000, Córdoba, Argentina
| | - H H Aragão Rêgo
- Departamento de Física, Instituto Federal de Educação, Ciência e Tecnologia do Maranhão, São Luís, MA, 65030-005, Brazil
| | - S V Buldyrev
- Department of Physics, Yeshiva University, 500 West 185th Street, New York, 10033, USA
| | - H E Stanley
- Center for Polymer Studies, Boston University, Boston, Massachusetts, 02215, USA
| | - L A Braunstein
- Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR)-Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata-CONICET, Funes, 3350, (7600) Mar del Plata, Argentina
- Center for Polymer Studies, Boston University, Boston, Massachusetts, 02215, USA
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15
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Structural instability of large-scale functional networks. PLoS One 2017; 12:e0181247. [PMID: 28727823 PMCID: PMC5519067 DOI: 10.1371/journal.pone.0181247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 06/28/2017] [Indexed: 11/19/2022] Open
Abstract
We study how large functional networks can grow stably under possible cascading overload failures and evaluated the maximum stable network size above which even a small-scale failure would cause a fatal breakdown of the network. Employing a model of cascading failures induced by temporally fluctuating loads, the maximum stable size nmax has been calculated as a function of the load reduction parameter r that characterizes how quickly the total load is reduced during the cascade. If we reduce the total load sufficiently fast (r ≥ rc), the network can grow infinitely. Otherwise, nmax is finite and increases with r. For a fixed r(< rc), nmax for a scale-free network is larger than that for an exponential network with the same average degree. We also discuss how one detects and avoids the crisis of a fatal breakdown of the network from the relation between the sizes of the initial network and the largest component after an ordinarily occurring cascading failure.
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16
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Norrenbrock C, Melchert O, Hartmann AK. Fragmentation properties of two-dimensional proximity graphs considering random failures and targeted attacks. Phys Rev E 2017; 94:062125. [PMID: 28085361 DOI: 10.1103/physreve.94.062125] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Indexed: 11/07/2022]
Abstract
The pivotal quality of proximity graphs is connectivity, i.e., all nodes in the graph are connected to one another either directly or via intermediate nodes. These types of graphs are often robust, i.e., they are able to function well even if they are subject to limited removal of elementary building blocks, as may occur for random failures or targeted attacks. Here, we study how the structure of these graphs is affected when nodes get removed successively until an extensive fraction is removed such that the graphs fragment. We study different types of proximity graphs for various node-removal strategies. We use different types of observables to monitor the fragmentation process, simple ones like the number and sizes of connected components and more complex ones like the hop diameter and the backup capacity, which is needed to make a network N-1 resilient. The actual fragmentation turns out to be described by a second-order phase transition. Using finite-size scaling analyses we numerically assess the threshold fraction of removed nodes, which is characteristic for the particular graph type and node deletion scheme; this suffices to decompose the underlying graphs.
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Affiliation(s)
- C Norrenbrock
- Institut für Physik, Universität Oldenburg, 26111 Oldenburg, Germany
| | - O Melchert
- Institut für Physik, Universität Oldenburg, 26111 Oldenburg, Germany
| | - A K Hartmann
- Institut für Physik, Universität Oldenburg, 26111 Oldenburg, Germany
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17
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Tang X, Liu J, Hao X. Mitigate Cascading Failures on Networks using a Memetic Algorithm. Sci Rep 2016; 6:38713. [PMID: 27934964 PMCID: PMC5146651 DOI: 10.1038/srep38713] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Accepted: 11/15/2016] [Indexed: 11/22/2022] Open
Abstract
Research concerning cascading failures in complex networks has become a hot topic. However, most of the existing studies have focused on modelling the cascading phenomenon on networks and analysing network robustness from a theoretical point of view, which considers only the damage incurred by the failure of one or several nodes. However, such a theoretical approach may not be useful in practical situation. Thus, we first design a much more practical measure to evaluate the robustness of networks against cascading failures, termed Rcf. Then, adopting Rcf as the objective function, we propose a new memetic algorithm (MA) named MA-Rcf to enhance network the robustness against cascading failures. Moreover, we design a new local search operator that considers the characteristics of cascading failures and operates by connecting nodes with a high probability of having similar loads. In experiments, both synthetic scale-free networks and real-world networks are used to test the efficiency and effectiveness of the MA-Rcf. We systematically investigate the effects of parameters on the performance of the MA-Rcf and validate the performance of the newly designed local search operator. The results show that the local search operator is effective, that MA-Rcf can enhance network robustness against cascading failures efficiently, and that it outperforms existing algorithms.
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Affiliation(s)
- Xianglong Tang
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an 710071, China
| | - Jing Liu
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an 710071, China
| | - Xingxing Hao
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an 710071, China
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18
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Mizutaka S, Tanizawa T. Robustness analysis of bimodal networks in the whole range of degree correlation. Phys Rev E 2016; 94:022308. [PMID: 27627318 DOI: 10.1103/physreve.94.022308] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Indexed: 11/07/2022]
Abstract
We present an exact analysis of the physical properties of bimodal networks specified by the two peak degree distribution fully incorporating the degree-degree correlation between node connections. The structure of the correlated bimodal network is uniquely determined by the Pearson coefficient of the degree correlation, keeping its degree distribution fixed. The percolation threshold and the giant component fraction of the correlated bimodal network are analytically calculated in the whole range of the Pearson coefficient from -1 to 1 against two major types of node removal, which are the random failure and the degree-based targeted attack. The Pearson coefficient for next-nearest-neighbor pairs is also calculated, which always takes a positive value even when the correlation between nearest-neighbor pairs is negative. From the results, it is confirmed that the percolation threshold is a monotonically decreasing function of the Pearson coefficient for the degrees of nearest-neighbor pairs increasing from -1 and 1 regardless of the types of node removal. In contrast, the node fraction of the giant component for bimodal networks with positive degree correlation rapidly decreases in the early stage of random failure, while that for bimodal networks with negative degree correlation remains relatively large until the removed node fraction reaches the threshold. In this sense, bimodal networks with negative degree correlation are more robust against random failure than those with positive degree correlation.
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Affiliation(s)
- Shogo Mizutaka
- School of Statistical Thinking, The Institute of Statistical Mathematics, Tachikawa 190-8562, Japan
| | - Toshihiro Tanizawa
- Kochi National College of Technology, 200-1 Monobe-Otsu, Nankoku, Kochi 783-8508, Japan
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19
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Zhou X, Peng W, Xu Z, Yang B. Hardness Analysis and Empirical Studies of the Relations among Robustness, Topology and Flow in Dynamic Networks. PLoS One 2015; 10:e0145421. [PMID: 26695517 PMCID: PMC4687921 DOI: 10.1371/journal.pone.0145421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Accepted: 12/03/2015] [Indexed: 11/19/2022] Open
Abstract
Network robustness is the ability of a network to maintain performance after disruption, thus it is an important index for network designers to refer to. Every actual network has its own topology structure, flow magnitude (scale) and flow distribution. How the robustness relates to these factors still remains unresolved. To analyze the relations, we first established a robustness problem model, studied the hardness of a special case of the model, and generated a lot of representative network instances. We conducted experiments on these instances, deleting 5% to 50% edges on each instance and found that the robustness of a network has an approximate linearity to its structural entropy and flow entropy, when the correlation coefficient between the structure and flow is fixed. We also found that robustness is unlikely to have a relation to the flow scale and edge scale in our model. The empirical studies thus can provide a way of quickly estimating the robustness of real-world networks by using the regression coefficients we obtained during the experiments. We conducted computation on a real-world dataset and got favorable results, which exhibited the effectiveness of the estimation.
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Affiliation(s)
- Xing Zhou
- National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, Hunan, China
| | - Wei Peng
- National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, Hunan, China
| | - Zhen Xu
- National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, Hunan, China
| | - Bo Yang
- National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha, Hunan, China
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Kawamoto H, Takayasu H, Jensen HJ, Takayasu M. Precise calculation of a bond percolation transition and survival rates of nodes in a complex network. PLoS One 2015; 10:e0119979. [PMID: 25885791 PMCID: PMC4401659 DOI: 10.1371/journal.pone.0119979] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2014] [Accepted: 02/01/2015] [Indexed: 11/28/2022] Open
Abstract
Through precise numerical analysis, we reveal a new type of universal loopless percolation transition in randomly removed complex networks. As an example of a real-world network, we apply our analysis to a business relation network consisting of approximately 3,000,000 links among 300,000 firms and observe the transition with critical exponents close to the mean-field values taking into account the finite size effect. We focus on the largest cluster at the critical point, and introduce survival probability as a new measure characterizing the robustness of each node. We also discuss the relation between survival probability and k-shell decomposition.
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Affiliation(s)
- Hirokazu Kawamoto
- Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Midori-ku, Yokohama, Japan
| | - Hideki Takayasu
- Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Midori-ku, Yokohama, Japan
- Sony Computer Science Laboratories, Shinagawa-ku, Tokyo, Japan
- Meiji Institute for Advanced Study of Mathematical Sciences, Meiji University, Nakano-ku, Tokyo, Japan
| | - Henrik Jeldtoft Jensen
- Department of Mathematics and Complexity & Networks Group, Imperial College, London, United Kingdom
| | - Misako Takayasu
- Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Midori-ku, Yokohama, Japan
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21
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Sampaio Filho CIN, Moreira AA, Andrade RFS, Herrmann HJ, Andrade JS. Mandala networks: ultra-small-world and highly sparse graphs. Sci Rep 2015; 5:9082. [PMID: 25765450 PMCID: PMC4357991 DOI: 10.1038/srep09082] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2014] [Accepted: 12/19/2014] [Indexed: 11/09/2022] Open
Abstract
The increasing demands in security and reliability of infrastructures call for the optimal design of their embedded complex networks topologies. The following question then arises: what is the optimal layout to fulfill best all the demands? Here we present a general solution for this problem with scale-free networks, like the Internet and airline networks. Precisely, we disclose a way to systematically construct networks which are robust against random failures. Furthermore, as the size of the network increases, its shortest path becomes asymptotically invariant and the density of links goes to zero, making it ultra-small world and highly sparse, respectively. The first property is ideal for communication and navigation purposes, while the second is interesting economically. Finally, we show that some simple changes on the original network formulation can lead to an improved topology against malicious attacks.
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Affiliation(s)
| | - André A. Moreira
- Departamento de Física, Universidade Federal do Ceará, 60451-970 Fortaleza, Ceará, Brazil
| | - Roberto F. S. Andrade
- Instituto de Física, Universidade Federal da Bahia, 40210-340 Salvador, Bahia, Brazil
| | - Hans J. Herrmann
- Departamento de Física, Universidade Federal do Ceará, 60451-970 Fortaleza, Ceará, Brazil
- Computational Physics for Engineering Materials, IfB, ETH Zurich, Schafmattstrasse 6, 8093 Zurich, Switzerland
| | - José S. Andrade
- Departamento de Física, Universidade Federal do Ceará, 60451-970 Fortaleza, Ceará, Brazil
- Computational Physics for Engineering Materials, IfB, ETH Zurich, Schafmattstrasse 6, 8093 Zurich, Switzerland
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22
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Melnik S, Porter MA, Mucha PJ, Gleeson JP. Dynamics on modular networks with heterogeneous correlations. CHAOS (WOODBURY, N.Y.) 2014; 24:023106. [PMID: 24985420 PMCID: PMC4000391 DOI: 10.1063/1.4869983] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2012] [Accepted: 03/18/2014] [Indexed: 05/07/2023]
Abstract
We develop a new ensemble of modular random graphs in which degree-degree correlations can be different in each module, and the inter-module connections are defined by the joint degree-degree distribution of nodes for each pair of modules. We present an analytical approach that allows one to analyze several types of binary dynamics operating on such networks, and we illustrate our approach using bond percolation, site percolation, and the Watts threshold model. The new network ensemble generalizes existing models (e.g., the well-known configuration model and Lancichinetti-Fortunato-Radicchi networks) by allowing a heterogeneous distribution of degree-degree correlations across modules, which is important for the consideration of nonidentical interacting networks.
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Affiliation(s)
- Sergey Melnik
- MACSI, Department of Mathematics & Statistics, University of Limerick, Ireland
| | - Mason A Porter
- Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom
| | - Peter J Mucha
- Department of Mathematics, Carolina Center for Interdisciplinary Applied Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599-3250, USA
| | - James P Gleeson
- MACSI, Department of Mathematics & Statistics, University of Limerick, Ireland
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23
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Watanabe S, Kabashima Y. Cavity-based robustness analysis of interdependent networks: influences of intranetwork and internetwork degree-degree correlations. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2014; 89:012808. [PMID: 24580282 DOI: 10.1103/physreve.89.012808] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2013] [Indexed: 06/03/2023]
Abstract
We develop a methodology for analyzing the percolation phenomena of two mutually coupled (interdependent) networks based on the cavity method of statistical mechanics. In particular, we take into account the influence of degree-degree correlations inside and between the networks on the network robustness against targeted (random degree-dependent) attacks and random failures. We show that the developed methodology is reduced to the well-known generating function formalism in the absence of degree-degree correlations. The validity of the developed methodology is confirmed by a comparison with the results of numerical experiments. Our analytical results indicate that the robustness of the interdependent networks depends on both the intranetwork and internetwork degree-degree correlations in a nontrivial way for both cases of random failures and targeted attacks.
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Affiliation(s)
- Shunsuke Watanabe
- Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 2268502, Japan
| | - Yoshiyuki Kabashima
- Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 2268502, Japan
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24
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Hasegawa T, Nemoto K. Hierarchical scale-free network is fragile against random failure. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2013; 88:062807. [PMID: 24483511 DOI: 10.1103/physreve.88.062807] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2013] [Indexed: 06/03/2023]
Abstract
We investigate site percolation in a hierarchical scale-free network known as the Dorogovtsev-Goltsev-Mendes network. We use the generating function method to show that the percolation threshold is 1, i.e., the system is not in the percolating phase when the occupation probability is less than 1. The present result is contrasted to bond percolation in the same network of which the percolation threshold is zero. We also show that the percolation threshold of intentional attacks is 1. Our results suggest that this hierarchical scale-free network is very fragile against both random failure and intentional attacks. Such a structural defect is common in many hierarchical network models.
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Affiliation(s)
- Takehisa Hasegawa
- Graduate School of Information Science, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Sendai, Miyagi 980-8579, Japan
| | - Koji Nemoto
- Department of Physics, Hokkaido University, Kita 10 Nisi 8, Kita-ku, Sapporo, Hokkaido 060-0810, Japan
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25
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Ichinose G, Tenguishi Y, Tanizawa T. Robustness of cooperation on scale-free networks under continuous topological change. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2013; 88:052808. [PMID: 24329319 DOI: 10.1103/physreve.88.052808] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2013] [Indexed: 06/03/2023]
Abstract
In this paper, we numerically investigate the robustness of cooperation clusters in prisoner's dilemma played on scale-free networks, where the network topologies change by continuous removal and addition of nodes. Each removal and addition can be either random or intentional. We therefore have four different strategies in changing network topology: random removal and random addition (RR), random removal and preferential addition (RP), targeted removal and random addition (TR), and targeted removal and preferential addition (TP). We find that cooperation clusters are most fragile against TR, while they are most robust against RP, even for large values of the temptation coefficient for defection. The effect of the degree mixing pattern of the network is not the primary factor for the robustness of cooperation under continuous change in network topology, which is quite different from the cases observed in static networks. Cooperation clusters become more robust as the number of links of hubs occupied by cooperators increase. Our results might infer the fact that a huge variety of individuals is needed for maintaining global cooperation in social networks in the real world where each node representing an individual is constantly removed and added.
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Affiliation(s)
- Genki Ichinose
- Department of Systems and Control Engineering, Anan National College of Technology, 265 Aoki Minobayashi, Anan, Tokushima 774-0017, Japan
| | - Yuto Tenguishi
- Department of Systems and Control Engineering, Anan National College of Technology, 265 Aoki Minobayashi, Anan, Tokushima 774-0017, Japan
| | - Toshihiro Tanizawa
- Department of Electrical Engineering and Information Science, Kochi National College of Technology, 200-1 Monobe-Otsu, Nankoku, Kochi 783-8508, Japan
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26
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Hasegawa T, Takaguchi T, Masuda N. Observability transitions in correlated networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2013; 88:042809. [PMID: 24229227 DOI: 10.1103/physreve.88.042809] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2013] [Indexed: 06/02/2023]
Abstract
Yang, Wang, and Motter [Phys. Rev. Lett. 109, 258701 (2012)] analyzed a model for network observability transitions in which a sensor placed on a node makes the node and the adjacent nodes observable. The size of the connected components comprising the observable nodes is a major concern of the model. We analyze this model in random heterogeneous networks with degree correlation. With numerical simulations and analytical arguments based on generating functions, we find that negative degree correlation makes networks more observable. This result holds true both when the sensors are placed on nodes one by one in a random order and when hubs preferentially receive the sensors. Finally, we numerically optimize networks with a fixed degree sequence with respect to the size of the largest observable component. Optimized networks have negative degree correlation induced by the resulting hub-repulsive structure; the largest hubs are rarely connected to each other, in contrast to the rich-club phenomenon of networks.
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Affiliation(s)
- Takehisa Hasegawa
- Graduate School of Information Science, Tohoku University, 6-3-09, Aramaki-Aza-Aoba, Sendai, Miyagi, 980-8579, Japan
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27
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Niño A, Muñoz-Caro C. Quantitative modeling of degree-degree correlation in complex networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2013; 88:032805. [PMID: 24125310 DOI: 10.1103/physreve.88.032805] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2013] [Revised: 07/29/2013] [Indexed: 06/02/2023]
Abstract
This paper presents an approach to the modeling of degree-degree correlation in complex networks. Thus, a simple function, Δ(k', k), describing specific degree-to-degree correlations is considered. The function is well suited to graphically depict assortative and disassortative variations within networks. To quantify degree correlation variations, the joint probability distribution between nodes with arbitrary degrees, P(k', k), is used. Introduction of the end-degree probability function as a basic variable allows using group theory to derive mathematical models for P(k', k). In this form, an expression, representing a family of seven models, is constructed with the needed normalization conditions. Applied to Δ(k', k), this expression predicts a nonuniform distribution of degree correlation in networks, organized in two assortative and two disassortative zones. This structure is actually observed in a set of four modeled, technological, social, and biological networks. A regression study performed on 15 actual networks shows that the model describes quantitatively degree-degree correlation variations.
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Affiliation(s)
- A Niño
- Center for Complex Networks Research, Northeastern University, Boston, Massachusetts 02115, USA
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28
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Mizutaka S, Yakubo K. Structural robustness of scale-free networks against overload failures. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2013; 88:012803. [PMID: 23944514 DOI: 10.1103/physreve.88.012803] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2013] [Indexed: 06/02/2023]
Abstract
We study the structural robustness of scale-free networks against overload failures induced by loads exceeding the node capacity, based on analytical and numerical approaches to the percolation problem in which a fixed number of nodes are removed according to the overload probability. Modeling fluctuating loads by random walkers in a network, we find that the degree dependence of the overload probability drastically changes with respect to the total load. We also elucidate that there exist two types of structural robustness of networks against overload failures. One is measured by the critical total load W(c) and the other is by the critical node removal fraction f(c). Enhancing the scale-free property, networks become fragile in both senses of W(c) and f(c). By contrast, increasing the node tolerance, scale-free networks become robust in the sense of the critical total load, while they come to be fragile in the sense of the critical node removal fraction. Furthermore, we show that these trends are not affected by degree-degree correlations, although assortative mixing makes networks robust in both senses of W(c) and f(c).
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Affiliation(s)
- Shogo Mizutaka
- Department of Applied Physics, Hokkaido University, Sapporo 060-8628, Japan.
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