The Multiple Instances of Node Centrality and their Implications on the Vulnerability of ISP Networks

The position of the nodes within a network topology largely determines the level of their involvement in various networking functions. Yet numerous node centrality indices, proposed to quantify how central individual nodes are in this respect, yield very different views of their relative signi ficance. Our first contribution in this paper is then an exhaustive sur vey and categorization of centrality indices along several att ributes including the type of information (local vs. global) and processing complexity required for their computation. We next study the seven most popular of those indices in the context of Internet vulnerability to address issues that remain under-explored in literature so far. First, we carry out a correlation study to assess the consistency of the node rankings those indices generate over ISP router-level topologies. For each pair of indices, we compute the full ranking correlation, which is the standard choice in literature, and the percentage overlap between the k top nodes. Then, we let these rankings guide the removal of highly central nodes and assess the impact on both the connectivity properties and traffic-carrying capacity of the network. Our results confirm that the top- k overlap predicts the comparative impact of indices on the network vulnerability better than the full-ranking correlation. Importantly, the locally computed degree centrality index approximates closely the global indices with the most dramatic impact on the traffic-carryin g ca- pacity; whereas, its approximative power in terms of connectivity is more topology-dependent. I. I NTRODUCTION networks addressing how the position and power of individual actors relate to their social interconnections and the way t hey interact with the rest of the network. Such sociological stu dies motivated the introduction of various sociological indice s, which sought to quantify the importance of nodes and their re- lationships. Bavela's work appears to be the first to have giv en a formal definition of node centrality in connected graphs as the sum of its own geodesics (shortest-path distances) to al l other nodes. This work triggered a large research thread and a huge number of publications in the area of centrality indices. Many of them proposed new indices (3) or adaptations of existing ones that expanded their applicability in a broader range of

[1]  Wenpu Xing,et al.  Weighted PageRank algorithm , 2004, Proceedings. Second Annual Conference on Communication Networks and Services Research, 2004..

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

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

[4]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[5]  Mark E. J. Newman A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[6]  Phillip Bonacich,et al.  Simultaneous group and individual centralities , 1991 .

[7]  Yong Li,et al.  ATTACK VULNERABILITY OF COMPLEX NETWORKS BASED ON LOCAL INFORMATION , 2007 .

[8]  Linton C. Freeman,et al.  Centered graphs and the structure of ego networks , 1982, Math. Soc. Sci..

[9]  Piet Van Mieghem,et al.  Robustness envelopes of networks , 2013, J. Complex Networks.

[10]  Vassilis Kostakos Temporal Graphs , 2014, Encyclopedia of Social Network Analysis and Mining.

[11]  Alex Bavelas A Mathematical Model for Group Structures , 1948 .

[12]  Ariel Orda,et al.  Shortest-path and minimum-delay algorithms in networks with time-dependent edge-length , 1990, JACM.

[13]  Robert L. Moxley,et al.  Determining Point-Centrality in Uncontrived Social Networks , 1974 .

[14]  Alessandro Vespignani,et al.  Large-scale topological and dynamical properties of the Internet. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Johan Bollen,et al.  Journal status , 2006, Scientometrics.

[16]  Chang-Yong Lee Correlations among centrality measures in complex networks , 2006, physics/0605220.

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

[18]  Afonso Ferreira,et al.  Computing Shortest, Fastest, and Foremost Journeys in Dynamic Networks , 2003, Int. J. Found. Comput. Sci..

[19]  Alon Itai,et al.  On the Complexity of Timetable and Multicommodity Flow Problems , 1976, SIAM J. Comput..

[20]  Mostafa Ammar,et al.  Routing in Space and Time in Networks with Predictable Mobility , 2004 .

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

[22]  Matthew Roughan,et al.  The Internet Topology Zoo , 2011, IEEE Journal on Selected Areas in Communications.

[23]  Vince Grolmusz,et al.  A note on the PageRank of undirected graphs , 2012, Inf. Process. Lett..

[24]  Alberto Leon-Garcia,et al.  On Traffic-Aware Betweenness and Network Criticality , 2010, 2010 INFOCOM IEEE Conference on Computer Communications Workshops.

[25]  Ying Ding,et al.  Applying centrality measures to impact analysis: A coauthorship network analysis , 2009 .

[26]  Ratul Mahajan,et al.  Measuring ISP topologies with rocketfuel , 2002, TNET.

[27]  D. Sade,et al.  Sociometrics of Macaca Mulatta III: n-path centrality in grooming networks , 1989 .

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

[29]  Mads Haahr,et al.  Social Network Analysis for Information Flow in Disconnected Delay-Tolerant MANETs , 2009, IEEE Transactions on Mobile Computing.

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

[31]  J. Anthonisse The rush in a directed graph , 1971 .

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

[33]  F. Harary,et al.  Eccentricity and centrality in networks , 1995 .

[34]  Ulrik Brandes,et al.  On variants of shortest-path betweenness centrality and their generic computation , 2008, Soc. Networks.

[35]  Ioannis Stavrakakis,et al.  On the Local Approximations of Node Centrality in Internet Router-Level Topologies , 2013, IWSOS.

[36]  Gert Sabidussi,et al.  The centrality index of a graph , 1966 .

[37]  Richard M. Karp,et al.  Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems , 1972, Combinatorial Optimization.

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

[39]  Olivier Bonaventure,et al.  Extracting Intra-domain Topology from mrinfo Probing , 2010, PAM.

[40]  Ljupco Kocarev,et al.  Vulnerability Assessment of Complex Networks Based on Optimal Flow Measurements under Intentional Node and Edge Attacks , 2009, ICT Innovations.

[41]  M. A. Beauchamp AN IMPROVED INDEX OF CENTRALITY. , 1965, Behavioral science.

[42]  Nils Petter Gleditsch,et al.  Structural parameters of graphs. A theoretical investigation , 1970 .

[43]  Stephen P. Borgatti,et al.  Centrality and network flow , 2005, Soc. Networks.

[44]  Yannick Rochat,et al.  Closeness Centrality Extended to Unconnected Graphs: the Harmonic Centrality Index , 2009 .

[45]  Lada A. Adamic,et al.  Search in Power-Law Networks , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[46]  George Pavlou,et al.  Cache "Less for More" in Information-Centric Networks , 2012, Networking.

[47]  Johan Bollen,et al.  Co-authorship networks in the digital library research community , 2005, Inf. Process. Manag..

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

[49]  Ross J. Anderson,et al.  Temporal node centrality in complex networks. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

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

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

[53]  Pan Hui,et al.  BUBBLE Rap: Social-Based Forwarding in Delay-Tolerant Networks , 2011 .

[54]  Cecilia Mascolo,et al.  Analysing information flows and key mediators through temporal centrality metrics , 2010, SNS '10.

[55]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[56]  Robert E. Tarjan,et al.  Linear Expected-Time Algorithms for Connectivity Problems , 1980, J. Algorithms.

[57]  Ioannis Stavrakakis,et al.  Centrality-driven scalable service migration , 2011, 2011 23rd International Teletraffic Congress (ITC).