By the same authors

Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification

Research output: Working paper

Standard

Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification. / Bai, Lu; Jiao, Yuhang; Cui, Lixin; Hancock, Edwin R.

2019. (arXiv).

Research output: Working paper

Harvard

Bai, L, Jiao, Y, Cui, L & Hancock, ER 2019 'Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification' arXiv. <https://arxiv.org/abs/1904.04238>

APA

Bai, L., Jiao, Y., Cui, L., & Hancock, E. R. (2019). Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification. (arXiv). https://arxiv.org/abs/1904.04238

Vancouver

Bai L, Jiao Y, Cui L, Hancock ER. Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification. 2019 Apr 6. (arXiv).

Author

Bai, Lu ; Jiao, Yuhang ; Cui, Lixin ; Hancock, Edwin R. / Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification. 2019. (arXiv).

Bibtex - Download

@techreport{ba94af6f5cce4c67980e9f6cab8c915a,
title = "Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification",
abstract = " In this paper, we develop a novel Aligned-Spatial Graph Convolutional Network (ASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized aligned grid structures, and define a new spatial graph convolution operation associated with the grid structures. We show that the proposed ASGCN model not only reduces the problems of information loss and imprecise information representation arising in existing spatially-based Graph Convolutional Network (GCN) models, but also bridges the theoretical gap between traditional Convolutional Neural Network (CNN) models and spatially-based GCN models. Moreover, the proposed ASGCN model can adaptively discriminate the importance between specified vertices during the process of spatial graph convolution, explaining the effectiveness of the proposed model. Experiments on standard graph datasets demonstrate the effectiveness of the proposed model. ",
keywords = "cs.LG, stat.ML",
author = "Lu Bai and Yuhang Jiao and Lixin Cui and Hancock, {Edwin R.}",
note = "arXiv admin note: text overlap with arXiv:1902.09936, arXiv:1809.01090",
year = "2019",
month = apr,
day = "6",
language = "Undefined/Unknown",
series = "arXiv",
type = "WorkingPaper",

}

RIS (suitable for import to EndNote) - Download

TY - UNPB

T1 - Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification

AU - Bai, Lu

AU - Jiao, Yuhang

AU - Cui, Lixin

AU - Hancock, Edwin R.

N1 - arXiv admin note: text overlap with arXiv:1902.09936, arXiv:1809.01090

PY - 2019/4/6

Y1 - 2019/4/6

N2 - In this paper, we develop a novel Aligned-Spatial Graph Convolutional Network (ASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized aligned grid structures, and define a new spatial graph convolution operation associated with the grid structures. We show that the proposed ASGCN model not only reduces the problems of information loss and imprecise information representation arising in existing spatially-based Graph Convolutional Network (GCN) models, but also bridges the theoretical gap between traditional Convolutional Neural Network (CNN) models and spatially-based GCN models. Moreover, the proposed ASGCN model can adaptively discriminate the importance between specified vertices during the process of spatial graph convolution, explaining the effectiveness of the proposed model. Experiments on standard graph datasets demonstrate the effectiveness of the proposed model.

AB - In this paper, we develop a novel Aligned-Spatial Graph Convolutional Network (ASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized aligned grid structures, and define a new spatial graph convolution operation associated with the grid structures. We show that the proposed ASGCN model not only reduces the problems of information loss and imprecise information representation arising in existing spatially-based Graph Convolutional Network (GCN) models, but also bridges the theoretical gap between traditional Convolutional Neural Network (CNN) models and spatially-based GCN models. Moreover, the proposed ASGCN model can adaptively discriminate the importance between specified vertices during the process of spatial graph convolution, explaining the effectiveness of the proposed model. Experiments on standard graph datasets demonstrate the effectiveness of the proposed model.

KW - cs.LG

KW - stat.ML

M3 - Working paper

T3 - arXiv

BT - Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification

ER -