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Characterization ofcomplex networks: Asurvey ofmeasurements Luciano da Fontoura CostaFrancisco A. RodriguesGonzalo Travieso2007 год

Characterization of complex networks: A survey of measurements
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Аннотация: Abstract Each complex network (or class of networks) presents specific topological features which characterize its connectivity and highly influence the dynamics of processes executed on the network. The analysis, discrimination, and synthesis of complex networks therefore rely on the use of measurements capable of expressing the most relevant topological features. This article presents a survey of such measurements. It includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements. Important related issues covered in this work comprise the representation of the evolution of complex networks in terms of trajectories in several measurement spaces, the analysis of the correlations between some of the most traditional measurements, perturbation analysis, as well as the use of multivariate statistics for feature selection and network classification. Depending on the network and the analysis task one has in mind, a specific set of features may be chosen. It is hoped that the present survey will help the proper application and interpretation of measurements. Acknowledgements We are grateful to Lucas Antiqueira, Carlos A.-A. Castillo-Ocaranza, Ernesto Estrada, A. Díaz-Guilera, Shalev Itzkovitz, Marcus Kaiser, Xiang Lee, Jon Machta, Adilson E. Motter, Osvaldo N. Oliveira-Jr, Andrea Scharnhorst, Matheus Viana, and Duncan Watts for comments and suggestions. Luciano da F. Costa is grateful to FAPESP (procs. 99/12765-2 and 05/00587-5), CNPq (proc. 308231/03-1) and the Human Frontier Science Program (RGP39/2002) for financial support. Francisco A. Rodrigues is grateful to FAPESP (proc. 04/00492-1) and Paulino R. Villas Boas is grateful to CNPq (proc. 141390/2004-2). Notes †Two subgraphs are topologically equivalent if the only difference is the weight of the existing edges. †Note that is identical to the average hierarchical clustering coefficient at the first level. †Notice that ⟨ k 2 (i)⟩ (average taken over all vertices i in the network) depends on the network connectivity. †Otherwise, orthogonal eigenvectors can still be assigned to repeated eigenvalues. †Optimal performance is guaranteed in case the involved mass and conditional properties are completely known (see section 19 and Citation68, Citation69). †Each Delaunay triangulation has as dual a Voronoi tessellation. Each vertex in the former structure is associated to one of the sides of the Voronoi cells, and vice-versa (e.g. Citation219).
Год издания: 2007
Авторы: Luciano da Fontoura Costa, Francisco A. Rodrigues, Gonzalo Travieso, Paulino Ribeiro Villas-Boas
Издательство: Taylor & Francis
Источник: Advances In Physics
Ключевые слова: Complex Network Analysis Techniques, Opinion Dynamics and Social Influence, Gene Regulatory Network Analysis
Другие ссылки: Advances In Physics (HTML)
arXiv (Cornell University) (PDF)
arXiv (Cornell University) (HTML)
arXiv (Cornell University) (PDF)
arXiv (Cornell University) (HTML)
arXiv (Cornell University) (PDF)
DataCite API (HTML)