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

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.

[1]  M E J Newman Assortative mixing in networks. , 2002, Physical review letters.

[2]  Petter Holme,et al.  Onion structure and network robustness , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Reka Albert,et al.  Mean-field theory for scale-free random networks , 1999 .

[4]  Filippo Radicchi,et al.  Beyond the locally treelike approximation for percolation on real networks. , 2016, Physical review. E.

[5]  Nina H. Fefferman,et al.  Simple and efficient self-healing strategy for damaged complex networks , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Hans J. Herrmann,et al.  Mandala Networks: ultra-small-world and highly sparse graphs , 2015, Scientific Reports.

[7]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[8]  Yukio Hayashi,et al.  Asymptotic behavior of the node degrees in the ensemble average of adjacency matrix , 2015, Network Science.

[9]  A. Saxenian The New Argonauts: Regional Advantage in a Global Economy , 1994 .

[10]  Alexandre Beaudet,et al.  The Toyota Group and the Aisin Fire , 1998 .

[11]  Nitesh V. Chawla,et al.  Range-limited Centrality Measures in Complex Networks , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Vijay V. Vazirani,et al.  Approximation Algorithms , 2001, Springer Berlin Heidelberg.

[13]  Hans J. Herrmann,et al.  Mitigation of malicious attacks on networks , 2011, Proceedings of the National Academy of Sciences.

[14]  Hernán A. Makse,et al.  Influence maximization in complex networks through optimal percolation , 2015, Nature.

[15]  Sang Geun Hahn,et al.  Bypass rewiring and robustness of complex networks. , 2016, Physical review. E.

[16]  Yukio Hayashi,et al.  Growing Self-Organized Design of Efficient and Robust Complex Networks , 2014, 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems.

[17]  Harry Eugene Stanley,et al.  Robustness of onion-like correlated networks against targeted attacks , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[19]  Massimo Marchiori,et al.  Error and attacktolerance of complex network s , 2004 .

[20]  Hai-Jun Zhou,et al.  Identifying optimal targets of network attack by belief propagation , 2016, Physical review. E.

[21]  K. Hashimoto Zeta functions of finite graphs and representations of p-adic groups , 1989 .

[22]  T. Nishiguchi,et al.  Case Study The Toyota Group and the Aisin Fire 49 , 2008 .

[23]  Hai-Jun Zhou,et al.  Spin glass approach to the feedback vertex set problem , 2013, 1307.6948.

[24]  Yukio Hayashi,et al.  Spatially self-organized resilient networks by a distributed cooperative mechanism , 2016, ArXiv.