research-article

Graph Pooling in Graph Neural Networks with Node Feature Correlation

Authors:

Jianjian Jiang,

Zhengmin LiAuthors Info & Claims

DSIT 2020: Proceedings of the 3rd International Conference on Data Science and Information Technology

Pages 105 - 110

https://doi.org/10.1145/3414274.3414490

Published: 26 August 2020 Publication History

Abstract

Because of the excellent performance of convolutional neural network in computer vision and natural language processing, we extend convolution operation to graph data and to define it as graph convolution. Different from node classification tasks, graph classification tasks need to pay attention to global information of graphs, which requires graph pooling mechanism to extract global information. Recently, Many researchers are devoted to the study of graph pooling and then proposed diversity of graph pooling models. However, in the graph classification tasks, these graph pooling methods are general and the graph classification accuracy still has room to improvement. Therefore, we propose the covariance pooling (CovPooling) to improve the classification accuracy of graph data sets. CovPooling uses node feature correlation to learn hierarchical representation of a graph. Our graph pooling utilizes node information and graph topology. Experiments show that our pooling module can be integrated into multiple graph convolution layers and achieve state-of-the-art performance in some datasets.

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Cited By

Wang YHou WSheng NZhao ZLiu JHuang LWang J(2024)Graph pooling in graph neural networks: methods and their applications in omics studiesArtificial Intelligence Review10.1007/s10462-024-10918-957:11Online publication date: 16-Sep-2024
https://doi.org/10.1007/s10462-024-10918-9
Xie JYing R(2022)Fea2Fea: Exploring Structural Feature Correlations via Graph Neural NetworksMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-030-93736-2_19(238-257)Online publication date: 17-Feb-2022
https://doi.org/10.1007/978-3-030-93736-2_19

Index Terms

Graph Pooling in Graph Neural Networks with Node Feature Correlation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Other conferences

DSIT 2020: Proceedings of the 3rd International Conference on Data Science and Information Technology

July 2020

261 pages

ISBN:9781450376044

DOI:10.1145/3414274

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Natl University of Singapore: National University of Singapore
SKKU: SUNGKYUNKWAN UNIVERSITY

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 August 2020

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Funding Sources

the Scientific and Technological Projects of Qingyuan
the Nature Science Foundation of China
Guangdong Provincial Key Laboratory Project of Intellectual Property and Big Data

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DSIT 2020

DSIT 2020: 2020 3rd International Conference on Data Science and Information Technology

July 24 - 26, 2020

Xiamen, China

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DSIT 2020 Paper Acceptance Rate 40 of 97 submissions, 41%;

Overall Acceptance Rate 114 of 277 submissions, 41%

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Cited By

Wang YHou WSheng NZhao ZLiu JHuang LWang J(2024)Graph pooling in graph neural networks: methods and their applications in omics studiesArtificial Intelligence Review10.1007/s10462-024-10918-957:11Online publication date: 16-Sep-2024
https://doi.org/10.1007/s10462-024-10918-9
Xie JYing R(2022)Fea2Fea: Exploring Structural Feature Correlations via Graph Neural NetworksMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-030-93736-2_19(238-257)Online publication date: 17-Feb-2022
https://doi.org/10.1007/978-3-030-93736-2_19

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