Identification of "Die Hard" Nodes in Complex Networks: A Resilience Approach

The topology of a network defines the structure on which physical processes dynamically evolve. Even though the topological analysis of these networks has revealed important properties about their organization, the components of real complex networks can exhibit other significant characteristics. In this work we focus in particular on the distribution of the weights associated to the links. Here, a novel metric is proposed to quantify the importance of both nodes and links in weighted scale-free networks in relation to their resilience. The resilience index takes into account the complete connectivity patterns of each node with all the other nodes in the network and is not correlated with other centrality metrics in heterogeneous weight distributions.

[1]  Martin G. Everett,et al.  A Graph-theoretic perspective on centrality , 2006, Soc. Networks.

[2]  Alessandro Vespignani,et al.  Characterization and modeling of weighted networks , 2005 .

[3]  V. Gol'dshtein,et al.  Vulnerability and Hierarchy of Complex Networks , 2004, cond-mat/0409298.

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

[5]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[6]  Massimo Marchiori,et al.  Vulnerability and protection of infrastructure networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[8]  P. Bonacich Power and Centrality: A Family of Measures , 1987, American Journal of Sociology.

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

[10]  R. Langer,et al.  Where a pill won't reach. , 2003, Scientific American.

[11]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[12]  John Skvoretz,et al.  Node centrality in weighted networks: Generalizing degree and shortest paths , 2010, Soc. Networks.

[13]  M. Newman Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[15]  Igor Mishkovski,et al.  Vulnerability of complex networks , 2011 .

[16]  U. Brandes A faster algorithm for betweenness centrality , 2001 .

[17]  V. Latora,et al.  Multiscale vulnerability of complex networks. , 2007, Chaos.

[18]  G. Bianconi Emergence of weight-topology correlations in complex scale-free networks , 2004, cond-mat/0412399.

[19]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[20]  Alessandro Vespignani,et al.  Vulnerability of weighted networks , 2006, physics/0603163.