Graph Drawing Using Dimension Reduction Methods

被引：1

作者：

Burianek, Tomas ^{[1
]}

Zaoralek, Lukas ^{[1
]}

Snasel, Vaclav ^{[1
,2
]}

Peterek, Tomas ^{[2
]}

机构：

[1] VSB Tech Univ Ostrava, Dept Comp Sci, FEECS, Ostrava 70833, Czech Republic

[2] VSB Tech Univ Ostrava, IT4innovat, Ostrava 70833, Czech Republic

来源：

AFRO-EUROPEAN CONFERENCE FOR INDUSTRIAL ADVANCEMENT, AECIA 2014 | 2015年 / 334卷

关键词：

Graph Drawing; Kamada and Kawai; Dimension Reduction; Principal Component Analysis; Stochastic Neighbor Embedding; Factor Analysis; Diffusion Maps;

D O I：

10.1007/978-3-319-13572-4_17

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Graphs are common data structures in computer science used to capture relations between set of objects. Graph drawing is a visual representation of the graph in more readable form, usually with vertices projected into R-2 space. Dimension reduction methods are designed to transform original data in high-dimensional space into a new data in lower-dimensional space. This work is focused on selected dimension reduction methods and their use to obtain sensible graph drawing from the original graph. Results of the dimension reduction methods are discussed and compared to each other and also to the classical approach presented by Kamada and Kawai.

引用

页码：205 / 214

页数：10

共 27 条

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