Drawing graphs. - Methods and models PDF

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Graph drawing is a dynamic and rapidly growing subfield of computer science and mathematics. It comprises all aspects of visualizing structural relations between objects. The range of topics dealt with extends from graph theory, graph algorithms, geometry, and topology to visual languages, visual perception, and information visualization, and to computer-human interaction and graphics design. The automated generation of graph drawings has important consequences for many subfields of computer science as well as for a broad variety of interdisciplinary application fields. This monograph gives a systematic overview of graph drawing and introduces the reader gently to the state of the art in the area. The presentation concentrates on algorithmic aspects, with an emphasis on interesting visualization problems with elegant solutions. Much attention is paid to a uniform style of writing and presentation, consistent terminology, and complementary coverage of the relevant issues throughout the 10 chapters. An overview of existing graph drawing systems, a comprehensive bibliography, and a subject index round off the presentation.This tutorial is ideally suited as an introduction for newcorners to graph drawing. Ambitioned practitioners and researchers active in the area will find it a valuable source of reference and information.

ISBN: 9783540420620

TAILLE DU FICHIER: 2,67 MB

DATE DE PUBLICATION: 2001-Jul-10

AUTEUR: Dorothea Wagner

TÉLÉCHARGER LIRE EN LIGNE

Graph drawing is a dynamic and rapidly growing subfield of computer science and mathematics. It comprises all aspects of visualizing structural relations between objects. The range of topics dealt with extends from graph theory, graph algorithms, geometry, and topology to visual languages, visual perception, and information visualization, and to computer-human interaction and graphics design.

statnet is a suite of software packages for network analysis and modeling, that allows for the estimation, evaluation, and simulation of network models, as well as network analysis and visualization. The network models include exponential random graph models (ERGMs) and latent variable models. Model fitting and evaluation is driven by a core of appropriate MCMC algorithms.

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