Clairvoyant targeted attack on complex networks

Complex networks' resilience against attacks represents a crucial issue in terms of network structure integrity. We investigate the effect of removing nodes on the network diameter in the case of a simultaneous targeted attack and sequential targeted attack. The analysis has been implemented on several network instances, taking into account different centrality measures and clustering coefficients values. Empirical networks have also been observed to compare the effects of the two removal schemes. According to classical literature, we assume that the network attacker has a wide-ranging knowledge of the system. It can be defined as clairvoyant since it knows, a priori, of all the characteristics of the problem's instances. This awareness is not always applicable when real networks are characterised by a dynamic environment. Hence, we distinguish between clairvoyant and non-clairvoyant attacks.

[1]  Shi Xiao,et al.  Tolerance of intentional attacks in complex communication networks , 2008, IEEE Communications Magazine.

[2]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

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

[4]  D S Callaway,et al.  Network robustness and fragility: percolation on random graphs. , 2000, Physical review letters.

[5]  Beom Jun Kim,et al.  Attack vulnerability of complex networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Paul Erdös,et al.  On random graphs, I , 1959 .

[7]  Hiroki Sayama,et al.  Optimization of robustness and connectivity in complex networks. , 2003, Physical review letters.

[8]  Guido Caldarelli,et al.  Scale-Free Networks , 2007 .

[9]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[10]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[11]  A. Barabasi,et al.  Universal resilience patterns in complex networks , 2016, Nature.

[12]  C. S. Holling.  Resilience and Stability of Ecological Systems , 1973 .

[13]  S. Havlin,et al.  Breakdown of the internet under intentional attack. , 2000, Physical review letters.

[14]  Giovanna Ferraro,et al.  Revealing correlations between structure and innovation attitude in inter-organisational innovation networks , 2015 .

[15]  Guanrong Chen,et al.  Complex networks: small-world, scale-free and beyond , 2003 .

[16]  Matteo Cinelli,et al.  Some Insights into the Relevance of Nodes’ Characteristics in Complex Network Structures , 2016 .

[17]  Antonio Iovanella,et al.  Exploiting incomplete information to manage multiprocessor tasks with variable arrival rates , 2008, Comput. Oper. Res..

[18]  Guido Caldarelli,et al.  Robustness and Assortativity for Diffusion-like Processes in Scale- free Networks , 2012 .

[19]  Andrei Z. Broder,et al.  Graph structure in the Web , 2000, Comput. Networks.

[20]  Giovanna Ferraro,et al.  Organizing Collaboration in Inter-Organizational Innovation Networks, from Orchestration to Choreography , 2015 .

[21]  Cohen,et al.  Resilience of the internet to random breakdowns , 2000, Physical review letters.

[22]  S. Brenner,et al.  The structure of the nervous system of the nematode Caenorhabditis elegans. , 1986, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[23]  Shi Xiao,et al.  Effects of node protections against intentional attack in complex communication networks , 2011, 2011 8th International Workshop on the Design of Reliable Communication Networks (DRCN).

[24]  Ulrik Brandes,et al.  What is network science? , 2013, Network Science.

[25]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[26]  Shin-Ming Cheng,et al.  Sequential Defense Against Random and Intentional Attacks in Complex Networks , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Giovanna Ferraro,et al.  On the influence of nodes' characteristic in inter-organisational innovation networks structure , 2016 .

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

[29]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[30]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[31]  T. Killingback,et al.  Attack Robustness and Centrality of Complex Networks , 2013, PloS one.