BCGL: Binary Classification-Based Graph Layout-Reference-Cited by-同舟云学术

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BCGL: Binary Classification-Based Graph Layout

Published:2022-09-01 Issue:9 Volume:E105.D Page:1610-1619
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

YAN Kai¹,ZHAO Tiejun¹,YANG Muyun¹

Affiliation:

1. School of Computer Science and Technology, Harbin Institute of Technology

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E105.D/9/E105.D_2021EDP7260/_pdf

Reference44 articles.

1. [1] Y. Hu and L. Shi, “Visualizing large graphs,” Wiley Interdisciplinary Reviews: Computational Statistics, vol.7, no.2, pp.115-136, 2015. 10.1002/wics.1343

2. [2] O.H. Kwon, T. Crnovrsanin, and K.L. Ma, “What would a graph look like in this layout? a machine learning approach to large graph visualization,” IEEE Trans. Vis. Comput. Graphics, vol.24, no.1, pp.478-488, Jan. 2017. 10.1109/TVCG.2017.2743858

3. [3] B. Lee, C. Plaisant, C.S. Parr, J.D. Fekete, and N. Henry, “Task taxonomy for graph visualization,” Proc. 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization, pp.1-5, May 2006. 10.1145/1168149.1168168

4. [4] T. Von Landesberger, A. Kuijper, T. Schreck, J. Kohlhammer, J.J. van Wijk, J.D. Fekete, and D.W. Fellner, “Visual analysis of large graphs: state-of-the-art and future research challenges,” Computer Graphics Forum, vol.30, no.6, pp.1719-1749, Wiley Online Library, Sept. 2011. 10.1111/j.1467-8659.2011.01898.x

5. [5] G. Xu, Z. Song, Y. Wang, D. Lin, J. Chen, T. Mao, and W. Xu, “A graph layout framework combining t-distributed neighbor retrieval visualizer and energy models,” IEEE Access, vol.7, pp.27515-27525, 2019. 10.1109/ACCESS.2019.2900358

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1. A grammar-based layout method for graph models;International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023);2024-01-09

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