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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.

- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.9 pp.1610-1619

- Publication Date
- 2022/09/01

- Publicized
- 2022/05/30

- Online ISSN
- 1745-1361

- DOI
- 10.1587/transinf.2021EDP7260

- Type of Manuscript
- PAPER

- Category
- Computer Graphics

Kai YAN

Harbin Institute of Technology

Tiejun ZHAO

Harbin Institute of Technology

Muyun YANG

Harbin Institute of Technology

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

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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

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@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},}

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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 -