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- W2487718087 abstract "We present a new method of quantitative graph analysis and visualization based on vertex centrality measures and distance matrices. After generating distance k-graphs and collecting frequency information about their vertex descriptors, we obtain generic, multidimensional representation of a graph, invariant to graph isomorphism. The histograms of vertex centrality measures, organized in a form of B-matrices, allow to capture subtle changes in network structure during its evolution and provide robust tool for graph comparison and classification. We show that different types of B-matrices and their extensions are useful in graph analysis tasks performed on benchmark complex networks from Koblenz and IAM datasets. We compare the results obtained for proposed B-matrix extensions with performance of other state-of-art graph descriptors showing that our method is superior to others." @default.
- W2487718087 created "2016-08-23" @default.
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- W2487718087 date "2016-01-01" @default.
- W2487718087 modified "2023-09-25" @default.
- W2487718087 title "A Method of Analysis and Visualization of Structured Datasets Based on Centrality Information" @default.
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- W2487718087 doi "https://doi.org/10.1007/978-3-319-39384-1_37" @default.
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