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A new design principle of robust onion-like networks self-organized in growth

Published online by Cambridge University Press:  06 November 2017

YUKIO HAYASHI
YUKIO HAYASHI*
Affiliation:
Japan Advanced Institute of Science and Technology, Ishikawa, Japan (e-mail: [email protected])
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Abstract

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Today's economy, production activity, and our life are sustained by social and technological network infrastructures, while new threats of network attacks by destructing loops have been found recently in network science. We inversely take into account the weakness, and propose a new design principle for incrementally growing robust networks. The networks are self-organized by enhancing interwoven long loops. In particular, we consider the range-limited approximation of linking by intermediations in a few hops, and show the strong robustness in the growth without degrading efficiency of paths. Moreover, we demonstrate that the tolerance of connectivity is reformable even from extremely vulnerable real networks according to our proposed growing process with some investment. These results may indicate a prospective direction to the future growth of our network infrastructures.

Keywords

coexistence of efficiency and robustnessonion-like structurelong-distance relationsinterwoven loopsunselfish self-organization
Type
Research Article
Information
Network Science , Volume 6 , Issue 1 , March 2018 , pp. 54 - 70
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © Cambridge University Press 2017

References

Albert, R., Jeong, H. & Barabási, A.-L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382.Google Scholar
Barabási, A.-L., Albert, R., & Jeong, H. (1999). Mean-filed theory for scale-free random networks. Physica A, 272, 173–187.CrossRefGoogle Scholar
Ercsey-Ravasz, M., Lichtenwalter, R., Chawla, N. V., & Toroczkai, Z. (2012). Range-limited centrality measures in complex networks. Physical Review E, 85, 066103.CrossRefGoogle ScholarPubMed
Gallos, L. K., & Fefferman, N. H. (2015). Simple and efficient self-healing strategy for damaged complex networks. Physical Review E, 92, 052806.Google Scholar
Hashimoto, K. (1989). Zeta functions of finite graphs and representations of p-adic groups. Advanced Studies in Pure Mathematics, 15, 211–280.CrossRefGoogle Scholar
Hayashi, Y. (2014). Growing self-organized design of efficient and robust complex networks. In Proceedings of the IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO '14). doi:10.1109/SASO.2014.17 50-59, arXiv:1411.7719.Google Scholar
Hayashi, Y. (2016a). Spatially self-organized resilient networks by a distributed cooperative mechanism. Physica A, 457, 255–269.CrossRefGoogle Scholar
Hayashi, Y. (2016b) Asymptotic behavior of the node degrees in the ensemble average of adjacency matrix. Network Science, 4 (3), 385–399.Google Scholar
Karp, R. M. (1972). Reducibility among combinatorial problems. In Miller, E., Thatcher, J. W., & Bohlinger, J. D. (Eds.), Complexity of Computer Computations (pp. 85–103). New York: Plenum Press.Google Scholar
Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD, International Conference on Knowledge Discovery and Data Mining, pp. 137–146.Google Scholar
Morone, F., & Makse, H. A. (2015) Influence maximization in complex networks through optimal percolation. Nature, 524, 65–68. Supplementary information http://www.nature.com/nature/journal/v524/n7563/extref/nature14604-s1.pdf Google Scholar
Mugisha, S., & Zhou, H.-J. (2016). Identifying optimal targets of network attack by belief propagation. Physical Review E, 94, 012305.Google Scholar
Newman, M. E. J. (2002). Assortative mixing in networks. Physical Review Letters, 89, 208701.Google Scholar
Nishiguchi, T. (2007). Global neighborhoods: Strategies of successful organizational networks. Tokyo: NTT Publishing (in Japanese). See also http://hitotsubashiiir-en.blogspot.jp/2012/12/researcher-profile-nishiguchi-toshihiro.html Google Scholar
Nishiguchi, T. & Beaudet, A. (1998). Case study the Toyota group and the Aisin fire. Sloan Management Review, 40 (1), 49–59.Google Scholar
Park, J. & Hahn, S. G. Bypass rewiring and robustness of complex networks. Physical Review E, 94, 022310.Google Scholar
Radicchi, F., & Castellano, C. (2016). Beyond the locally treelike approximation for percolation on real networks. Physical Review E, 93, 030302(R).CrossRefGoogle ScholarPubMed
Sampaio Filho, C. I. N., Moreira, A. A., Andrade, R. S. F., Herrmann, H. J., & Andrade, J. S. Jr. (2015). Mandara networks: ultra-small-world and highly sparse graphs. Scientific Reports, 5, 9082.CrossRefGoogle Scholar
Saxenian, A. (2007). The new argonauts: Regional advantage in a global economy. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Schneider, C. M., Moreira, A. A., Andrade, J. S. Jr., Havlin, S., & Herrmann, H. J. (2011). Mitigation of malicious attacks on networks. Proceedings of the National Academy of Sciences of the United States of America, 108, 3838–3841.Google Scholar
Tanizawa, T., Havlin, S., & Stanley, H. E. (2012). Robustness of onion-like correlated networks against targeted attacks. Physical Review E, 85, 046109.CrossRefGoogle Scholar
Vazirani, V. V. (2001). Feedback vertex set. In Approximation algorithms, Chapter 6 (pp. 54–60). Berlin, Heidelberg: Springer-Verlag.Google Scholar
Watts, D. J. & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442.Google Scholar
Wu, Z.-X. & Holme, P. (2011). Onion structure and network robustness. Physical Review E, 84, 026106.CrossRefGoogle ScholarPubMed
Zhou, H.-J. (2013). Spin glass approach to the feedback vertex set problem. European Physical Journal B, 86, 455.Google Scholar