Robustness of Networks with Skewed Degree Distributions under Strategic Node Protection

Previous studies on the robustness of networks against intentional attacks have suggested that protecting a small fraction of important nodes in a network significantly improves its robustness. In this paper, we analyze the robustness of networks under several strategic node protection schemes. Strategic node protection schemes select a small fraction of nodes as important nodes, using a network measure such as node centrality, and protect the important nodes to prevent them from being removed by intentional attacks. Our simulation results indicate that (1) strategic node protection significantly improves the robustness of networks with skewed degree distributions, (2) the efficiency of strategic node protection schemes is affected by the strength of community structure of the network being protected, and (3) strategic node protection based on betweenness centrality can effectively improve the robustness of networks regardless of the strength of community structure.

[1]  Martin H. Levinson.  Linked: The New Science of Networks , 2004 .

[2]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

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

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

[6]  Yilun Shang.  Robustness of scale-free networks under attack with tunable grey information , 2011 .

[7]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[8]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Jari Saramäki,et al.  Model of community emergence in weighted social networks , 2009, Comput. Phys. Commun..

[10]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[11]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[12]  Hiroyoshi Miwa,et al.  Design Method of Robust Networks against Performance Deterioration during Failures , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[13]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

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

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

[16]  Lada A. Adamic,et al.  The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.

[17]  Ernesto Estrada,et al.  Network robustness to targeted attacks. The interplay of expansibility and degree distribution , 2006 .

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

[19]  Hongzhong Deng,et al.  Vulnerability of complex networks under intentional attack with incomplete information , 2007 .

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

[21]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[22]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[23]  Albert-László Barabási,et al.  Internet: Diameter of the World-Wide Web , 1999, Nature.