Vulnerability metrics and analysis for communities in complex networks

This paper applies the problem of community detection in complex networks to identify sets of network elements that are critical to the connectivity of the network and its communities. Specifically, the paper defines a vulnerability set and value for each of the communities in a complex network. Also, for each community it identifies a value of relative vulnerability in comparison with the remaining communities. The approach allows to visualize/identify the critical elements of a complex network. This is an important first step for many recent problems arising in social networks, critical infrastructures and homeland security. By identifying these elements one can prioritize resource allocation to protect, interdict or improve performance in these types of systems. The sets and metrics introduced are illustrated with numerous examples and discussions. Based on the analysis of the examples the manuscript provides an intuitive description of a community's presence in the interior or periphery of a network.

[1]  Massimo Marchiori,et al.  LOCATING CRITICAL LINES IN HIGH-VOLTAGE ELECTRICAL POWER GRIDS , 2005, The Random and Fluctuating World.

[2]  ENRICO ZIO,et al.  A Flow Importance Measure with Application to an Italian Transmission Power System , 2009 .

[3]  Jari Saramäki,et al.  Limited resolution and multiresolution methods in complex network community detection , 2007, SPIE International Symposium on Fluctuations and Noise.

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

[5]  R. Hanneman Introduction to Social Network Methods , 2001 .

[6]  Masayuki Murata,et al.  Traffic dynamic in modularity structure of complex networks. , 2008, 2008 5th International Conference on Broadband Communications, Networks and Systems.

[7]  Benjamin H. Good,et al.  Performance of modularity maximization in practical contexts. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Martin Suter,et al.  Small World , 2002 .

[9]  Hui-jun Sun,et al.  Cascade and breakdown in scale-free networks with community structure. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Matthieu Latapy,et al.  Computing Communities in Large Networks Using Random Walks , 2004, J. Graph Algorithms Appl..

[11]  Mason A. Porter,et al.  Communities in Networks , 2009, ArXiv.

[12]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[13]  Enrico Zio,et al.  A Flow Importance Measure via Multiple-Objective Optimization and its Application to an Italian Transmission Power System , 2010 .

[14]  S. vanDongen Graph Clustering by Flow Simulation , 2000 .

[15]  M. Newman,et al.  Robustness of community structure in networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Y Yao,et al.  Decomposition of Large Scale Semantic Graphsvia an Efficient Communities Algorithm , 2008 .

[17]  Gregory Levitin,et al.  Optimal network protection against diverse interdictor strategies , 2011, Reliab. Eng. Syst. Saf..

[18]  Francesco Maffioli,et al.  Fishman's sampling plan for computing network reliability , 2001, IEEE Trans. Reliab..

[19]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Claudio M. Rocco Sanseverino,et al.  A holistic method for reliability performance assessment and critical components detection in complex networks , 2011 .

[21]  Enrico Zio,et al.  From complexity science to reliability efficiency: a new way of looking at complex network systems and critical infrastructures , 2007, Int. J. Crit. Infrastructures.

[22]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Massimo Marchiori,et al.  Economic small-world behavior in weighted networks , 2003 .

[24]  Duncan J. Watts,et al.  The Structure and Dynamics of Networks: (Princeton Studies in Complexity) , 2006 .

[25]  David Skillicorn,et al.  Using Matrix Decompositions for Data Mining (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series) , 2007 .

[26]  Enrico Zio,et al.  AN ANALYTICAL APPROACH TO THE SAFETY OF ROAD NETWORKS , 2008 .

[27]  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.

[28]  Jose Emmanuel Ramirez-Marquez,et al.  A Classification Tree Based Approach for the Development of Minimal Cut and Path Vectors of a Capacitated Network , 2007, IEEE Transactions on Reliability.

[29]  Claudio M. Rocco Sanseverino,et al.  Assessing the Vulnerability of a Power System Through a Multiple Objective Contingency Screening Approach , 2011, IEEE Transactions on Reliability.