By the same authors

Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

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

Machine Learning and Knowledge Discovery in Databases. ed. / Ulf Brefeld; Elisa Fromont; Andreas Hotho; Arno Knobbe; Marloes Maathuis; Céline Robardet. Cham : Springer International Publishing, 2020. p. 464-482 (Lecture Notes in Computer Science; Vol. 11906).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Bai, L, Jiao, Y, Cui, L & Hancock, ER 2020, Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification. in U Brefeld, E Fromont, A Hotho, A Knobbe, M Maathuis & C Robardet (eds), Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science, vol. 11906, Springer International Publishing, Cham, pp. 464-482. https://doi.org/10.1007/978-3-030-46150-8_28

APA

Bai, L., Jiao, Y., Cui, L., & Hancock, E. R. (2020). Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine Learning and Knowledge Discovery in Databases (pp. 464-482). (Lecture Notes in Computer Science; Vol. 11906). Springer International Publishing. https://doi.org/10.1007/978-3-030-46150-8_28

Vancouver

Bai L, Jiao Y, Cui L, Hancock ER. Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification. In Brefeld U, Fromont E, Hotho A, Knobbe A, Maathuis M, Robardet C, editors, Machine Learning and Knowledge Discovery in Databases. Cham: Springer International Publishing. 2020. p. 464-482. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-46150-8_28

Author

Bai, Lu ; Jiao, Yuhang ; Cui, Lixin ; Hancock, Edwin R. / Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification. Machine Learning and Knowledge Discovery in Databases. editor / Ulf Brefeld ; Elisa Fromont ; Andreas Hotho ; Arno Knobbe ; Marloes Maathuis ; Céline Robardet. Cham : Springer International Publishing, 2020. pp. 464-482 (Lecture Notes in Computer Science).

Bibtex - Download

@inproceedings{2fa1c1afe3fb4e439eb05070852fed9d,
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.",
author = "Lu Bai and Yuhang Jiao and Lixin Cui and Hancock, {Edwin R.}",
note = "{\textcopyright} Springer Nature Switzerland AG 2020. This is an author-produced version of the published paper. Uploaded in accordance with the publisher{\textquoteright}s self-archiving policy. Further copying may not be permitted; contact the publisher for details.",
year = "2020",
month = apr,
day = "30",
doi = "10.1007/978-3-030-46150-8_28",
language = "Undefined/Unknown",
isbn = "978-3-030-46150-8",
series = "Lecture Notes in Computer Science",
publisher = "Springer International Publishing",
pages = "464--482",
editor = "Ulf Brefeld and Elisa Fromont and Andreas Hotho and Arno Knobbe and Marloes Maathuis and C{\'e}line Robardet",
booktitle = "Machine Learning and Knowledge Discovery in Databases",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

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

AU - Bai, Lu

AU - Jiao, Yuhang

AU - Cui, Lixin

AU - Hancock, Edwin R.

N1 - © Springer Nature Switzerland AG 2020. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

PY - 2020/4/30

Y1 - 2020/4/30

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.

U2 - 10.1007/978-3-030-46150-8_28

DO - 10.1007/978-3-030-46150-8_28

M3 - Conference contribution

SN - 978-3-030-46150-8

T3 - Lecture Notes in Computer Science

SP - 464

EP - 482

BT - Machine Learning and Knowledge Discovery in Databases

A2 - Brefeld, Ulf

A2 - Fromont, Elisa

A2 - Hotho, Andreas

A2 - Knobbe, Arno

A2 - Maathuis, Marloes

A2 - Robardet, Céline

PB - Springer International Publishing

CY - Cham

ER -