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Graph Layouts by t‐SNE
Author(s) -
Kruiger J. F.,
Rauber P. E.,
Martins R. M.,
Kerren A.,
Kobourov S.,
Telea A. C.
Publication year - 2017
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13187
Subject(s) - computer science , embedding , graph layout , graph , theoretical computer science , benchmark (surveying) , dimensionality reduction , visualization , context (archaeology) , graph drawing , algorithm , artificial intelligence , paleontology , geodesy , biology , geography
We propose a new graph layout method based on a modification of the t‐distributed Stochastic Neighbor Embedding (t‐SNE) dimensionality reduction technique. Although t‐SNE is one of the best techniques for visualizing high‐dimensional data as 2D scatterplots, t‐SNE has not been used in the context of classical graph layout. We propose a new graph layout method, tsNET, based on representing a graph with a distance matrix, which together with a modified t‐SNE cost function results in desirable layouts. We evaluate our method by a formal comparison with state‐of‐the‐art methods, both visually and via established quality metrics on a comprehensive benchmark, containing real‐world and synthetic graphs. As evidenced by the quality metrics and visual inspection, tsNET produces excellent layouts.