In this article we present the Hayastan Shakarian (HS), a robustness index for complex networks. HS measures the impact of a network disconnection (edge) while comparing the sizes of the remaining connected components. Strictly speaking, the Hayastan Shakarian index is defined as edge removal that produces the maximal inverse of the size of the largest connected component divided by the sum of the sizes of the remaining ones. We tested our index in attack strategies where the nodes are disconnected in decreasing order of a specified metric. We considered using the Hayastan Shakarian cut (disconnecting the edge with max HS) and other well-known strategies as the higher betweenness centrality disconnection. All strategies were compared regarding the behavior of the robustness (R-index) during the attacks. In an attempt to simulate the internet backbone, the attacks were performed in complex networks with power-law degree distributions (scale-free networks). Preliminary results show that attacks based on disconnecting using the Hayastan Shakarian cut are more dangerous (decreasing the robustness) than the same attacks based on other centrality measures. We believe that the Hayastan Shakarian cut, as well as other measures based on the size of the largest connected component, provides a good addition to other robustness metrics for complex networks.
The idea of planning a network attack'' using centrality measures has captured the attention of researchers and practitioners nowadays. For instance, @cite_11 used bet -ween -ness-centrality () for planning a network attack, calculating the value for all nodes, ordering nodes from higher to lower , and then attacking (discarding) those nodes in that order. They have shown that disconnecting only two of the top -ranked nodes, their packet-delivery ratio is reduced to @math 10 @math 100+ @math 0 In the study of resilience after edge removing, @cite_13 study backup communication paths for network services defining that a network is '' given that ''.
@cite_13 @cite_11
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We develop a graph-theoretic model for service-oriented networks and propose metrics that quantify the resilience of such networks under node and edge failures. These metrics are based on the topological structure of the network and the manner in which services are distributed over the network. We present efficient algorithms to determine the maximum number of node and edge failures that can be tolerated by a given service-oriented network. These algorithms rely on known algorithms for computing minimum cuts in graphs. We also present efficient algorithms for optimally allocating services over a given network so that the resulting service-oriented network can tolerate single node or edge failures. These algorithms are derived through a careful analysis of the decomposition of the underlying network into appropriate types of connected components. As the Internet becomes increasingly important to all aspects of society, the consequences of disruption become increasingly severe. Thus it is critical to increase the resilience and survivability of the future network. We define resilience as the ability of the network to provide desired service even when challenged by attacks, large-scale disasters, and other failures. This paper describes a comprehensive methodology to evaluate network resilience using a combination of analytical and simulation techniques with the goal of improving the resilience and survivability of the Future Internet.