Drawing graphs by eigenvectors: theory and practice

by: Y Koren

Computers & Mathematics with Applications, Vol. 49, No. 11-12. (June 2005), pp. 1867-1888.

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Abstract

The spectral approach for graph visualization computes the layout of a graph using certain eigenvectors of related matrices. Two important advantages of this approach are an ability to compute optimal layouts (according to specific requirements) and a very rapid computation time. In this paper, we explore spectral visualization techniques and study their properties from different points of view. We also suggest a novel algorithm for calculating spectral layouts resulting in an extremely fast computation by optimizing the layout within a small vector space.

@article{citeulike:2458647, abstract = {The spectral approach for graph visualization computes the layout of a graph using certain eigenvectors of related matrices. Two important advantages of this approach are an ability to compute optimal layouts (according to specific requirements) and a very rapid computation time. In this paper, we explore spectral visualization techniques and study their properties from different points of view. We also suggest a novel algorithm for calculating spectral layouts resulting in an extremely fast computation by optimizing the layout within a small vector space.}, author = {Koren, Y. }, citeulike-article-id = {2458647}, doi = {10.1016/j.camwa.2004.08.015}, journal = {Computers \& Mathematics with Applications}, keywords = {drawing, eigenvectors, graph, spectral}, month = {June}, number = {11-12}, pages = {1867--1888}, posted-at = {2008-03-02 22:41:35}, priority = {0}, title = {Drawing graphs by eigenvectors: theory and practice}, url = {http://dx.doi.org/10.1016/j.camwa.2004.08.015}, volume = {49}, year = {2005} }

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TY - JOUR ID - citeulike:2458647 L3 - citeulike-article-id:2458647 TI - Drawing graphs by eigenvectors: theory and practice JF - Computers & Mathematics with Applications VL - 49 IS - 11-12 SP - 1867 EP - 1888 N2 - The spectral approach for graph visualization computes the layout of a graph using certain eigenvectors of related matrices. Two important advantages of this approach are an ability to compute optimal layouts (according to specific requirements) and a very rapid computation time. In this paper, we explore spectral visualization techniques and study their properties from different points of view. We also suggest a novel algorithm for calculating spectral layouts resulting in an extremely fast computation by optimizing the layout within a small vector space. KW - drawing KW - eigenvectors KW - graph KW - spectral AU - Koren, Y PY - 2005/06// UR - http://dx.doi.org/10.1016/j.camwa.2004.08.015 ER -

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