The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Reka letak graf mendedahkan struktur global atau tempatan data graf. Walau bagaimanapun, terdapat beberapa kajian untuk membantu pembaca membina semula graf daripada susun atur dengan lebih baik. Kertas kerja ini cuba menjana reka letak yang tepinya boleh diwujudkan semula. Kami merumuskan semula masalah reka letak graf sebagai masalah pengelasan tepi. Input adalah pasangan puncak, dan output adalah kewujudan tepi. Parameter yang boleh dilatih ialah koordinat yang dibentangkan bagi bucu. Kami mencadangkan rangka kerja susun atur graf berasaskan klasifikasi binari (BCGL) dalam kertas kerja ini. Reka letak ini bertujuan untuk mengekalkan struktur tempatan graf dan tidak memerlukan jumlah perhubungan persamaan bucu. Kami melaksanakan dua algoritma konkrit di bawah rangka kerja BCGL, menilai pendekatan kami pada pelbagai jenis set data dan membuat perbandingan dengan beberapa kaedah lain. Penilaian mengesahkan keupayaan BCGL dalam pemeliharaan kejiranan tempatan dan kualiti visualnya dengan beberapa metrik klasik.
Kai YAN
Harbin Institute of Technology
Tiejun ZHAO
Harbin Institute of Technology
Muyun YANG
Harbin Institute of Technology
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Salinan
Kai YAN, Tiejun ZHAO, Muyun YANG, "BCGL: Binary Classification-Based Graph Layout" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 9, pp. 1610-1619, September 2022, doi: 10.1587/transinf.2021EDP7260.
Abstract: Graph layouts reveal global or local structures of graph data. However, there are few studies on assisting readers in better reconstructing a graph from a layout. This paper attempts to generate a layout whose edges can be reestablished. We reformulate the graph layout problem as an edge classification problem. The inputs are the vertex pairs, and the outputs are the edge existences. The trainable parameters are the laid-out coordinates of the vertices. We propose a binary classification-based graph layout (BCGL) framework in this paper. This layout aims to preserve the local structure of the graph and does not require the total similarity relationships of the vertices. We implement two concrete algorithms under the BCGL framework, evaluate our approach on a wide variety of datasets, and draw comparisons with several other methods. The evaluations verify the ability of the BCGL in local neighborhood preservation and its visual quality with some classic metrics.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7260/_p
Salinan
@ARTICLE{e105-d_9_1610,
author={Kai YAN, Tiejun ZHAO, Muyun YANG, },
journal={IEICE TRANSACTIONS on Information},
title={BCGL: Binary Classification-Based Graph Layout},
year={2022},
volume={E105-D},
number={9},
pages={1610-1619},
abstract={Graph layouts reveal global or local structures of graph data. However, there are few studies on assisting readers in better reconstructing a graph from a layout. This paper attempts to generate a layout whose edges can be reestablished. We reformulate the graph layout problem as an edge classification problem. The inputs are the vertex pairs, and the outputs are the edge existences. The trainable parameters are the laid-out coordinates of the vertices. We propose a binary classification-based graph layout (BCGL) framework in this paper. This layout aims to preserve the local structure of the graph and does not require the total similarity relationships of the vertices. We implement two concrete algorithms under the BCGL framework, evaluate our approach on a wide variety of datasets, and draw comparisons with several other methods. The evaluations verify the ability of the BCGL in local neighborhood preservation and its visual quality with some classic metrics.},
keywords={},
doi={10.1587/transinf.2021EDP7260},
ISSN={1745-1361},
month={September},}
Salinan
TY - JOUR
TI - BCGL: Binary Classification-Based Graph Layout
T2 - IEICE TRANSACTIONS on Information
SP - 1610
EP - 1619
AU - Kai YAN
AU - Tiejun ZHAO
AU - Muyun YANG
PY - 2022
DO - 10.1587/transinf.2021EDP7260
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2022
AB - Graph layouts reveal global or local structures of graph data. However, there are few studies on assisting readers in better reconstructing a graph from a layout. This paper attempts to generate a layout whose edges can be reestablished. We reformulate the graph layout problem as an edge classification problem. The inputs are the vertex pairs, and the outputs are the edge existences. The trainable parameters are the laid-out coordinates of the vertices. We propose a binary classification-based graph layout (BCGL) framework in this paper. This layout aims to preserve the local structure of the graph and does not require the total similarity relationships of the vertices. We implement two concrete algorithms under the BCGL framework, evaluate our approach on a wide variety of datasets, and draw comparisons with several other methods. The evaluations verify the ability of the BCGL in local neighborhood preservation and its visual quality with some classic metrics.
ER -