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• W992689895 abstract "The topological structure of complex networks has fascinated researchers for several decades, and today we have a fairly good understanding of the types and reoccurring characteristics of many different complex networks. However, surprisingly little is known today about models to compare complex graphs, and quantitatively measure their similarity and dynamics. In the past a variety of node centralities, i.e., functions which assign values to nodes to represent their importance in the graph. Based on such centralities we propose a natural similarity measure for complex networks: the centrality distance $d_C$, the difference between two graphs with respect to a given node centrality $C$. Centrality distances can take the specific roles of the different nodes in the network into account, and have many interesting applications. As a case study, we investigate the evolution of networks with respect to centrality distances and their approximations based on closeness, betweenness, pagerank, clustering and degree centrality. In particular we study the dynamic behavior of six real-world social and physical networks and show that centrality distances can be used to effectively distinguish between randomly generated and actual evolutionary paths of dynamic complex networks." @default.
• W992689895 created "2016-06-24" @default.
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• W992689895 date "2015-06-04" @default.
• W992689895 modified "2023-10-16" @default.
• W992689895 title "Exploring the Graph of Graphs: Network Evolution and Centrality Distances." @default.
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• W992689895 hasPublicationYear "2015" @default.
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