Abstract
Centrality metrics have been used in various networks, such as communication, social, biological, geographic, or contact networks. In particular, they have been used in order to study and analyze targeted attack behaviors and investigated their effect on network resilience. Although a rich volume of centrality metrics has been developed for decades, a limited set of centrality metrics have been commonly in use. This paper aims to introduce various existing centrality metrics and discuss their applicabilities and performance based on the results obtained from extensive simulation experiments to encourage their use in solving various computing and engineering problems in networks.
Highlights
We implemented 56 centrality metrics and analyzed their effect on network resilience based on the size of the giant component when each centrality metric is used to model targeted attacks
We investigated the effect of each centrality metric on network resilience in terms of the size of the giant component
We found that if a centrality metric measures how well a node is connected with its close neighborhood, its impact upon removing the node with high centrality tends to be limited
Summary
The authors proved new complexity results of algorithms that were improved based on the relationships between variants Unlike these prior surveys [20], [21], [22], [24], [23], [25], our survey primarily focuses on the investigation of node centrality, graph centrality, and group-selection centrality in the context of the impact of centrality on network resilience under targeted attacks. KEY CONTRIBUTIONS & SCOPE Unlike the other state-of-the-art survey papers above, this survey paper makes the following key contributions: We discussed multidisciplinary concepts of centrality and its historical evolution in the research literature This provides insights on how centrality metrics have been applied in various kinds of networks, in particular their applicability in communication and social networks of interest to many engineers.
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