research-article

Dynamic Graph Learning Convolutional Networks for Semi-supervised Classification

Authors:

Changsheng XuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 17, Issue 1s

Article No.: 4, Pages 1 - 13

https://doi.org/10.1145/3412846

Published: 31 March 2021 Publication History

Abstract

Over the past few years, graph representation learning (GRL) has received widespread attention on the feature representations of the non-Euclidean data. As a typical model of GRL, graph convolutional networks (GCN) fuse the graph Laplacian-based static sample structural information. GCN thus generalizes convolutional neural networks to acquire the sample representations with the variously high-order structures. However, most of existing GCN-based variants depend on the static data structural relationships. It will result in the extracted data features lacking of representativeness during the convolution process. To solve this problem, dynamic graph learning convolutional networks (DGLCN) on the application of semi-supervised classification are proposed. First, we introduce a definition of dynamic spectral graph convolution operation. It constantly optimizes the high-order structural relationships between data points according to the loss values of the loss function, and then fits the local geometry information of data exactly. After optimizing our proposed definition with the one-order Chebyshev polynomial, we can obtain a single-layer convolution rule of DGLCN. Due to the fusion of the optimized structural information in the learning process, multi-layer DGLCN can extract richer sample features to improve classification performance. Substantial experiments are conducted on citation network datasets to prove the effectiveness of DGLCN. Experiment results demonstrate that the proposed DGLCN obtains a superior classification performance compared to several existing semi-supervised classification models.

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Cited By

Li MZhou THuang ZYang JYang JGong C(2023)Dynamic Weighted Adversarial Learning for Semi-Supervised Classification under Intersectional Class MismatchACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363531020:4(1-24)Online publication date: 2-Dec-2023
https://dl.acm.org/doi/10.1145/3635310
Luo XJu WGu YQin YYi SWu DLiu LZhang M(2023)Toward Effective Semi-supervised Node Classification with Hybrid Curriculum Pseudo-labelingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362652820:3(1-19)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3626528
Mai SXing SHe JZeng YHu H(2022)Multimodal Graph for Unaligned Multimodal Sequence Analysis via Graph Convolution and Graph PoolingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/354292719:2(1-24)Online publication date: 9-Jun-2022
https://dl.acm.org/doi/10.1145/3542927

Index Terms

Dynamic Graph Learning Convolutional Networks for Semi-supervised Classification
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Semi-supervised learning settings
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Semi-supervised learning

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17, Issue 1s

January 2021

353 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3453990

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 31 March 2021

Accepted: 01 July 2020

Revised: 01 July 2020

Received: 01 February 2020

Published in TOMM Volume 17, Issue 1s

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Major Scientific and Technological Projects of CNPC
Open Project Program of the National Laboratory of Pattern Recognition (NLPR)
Science and Technology Development Fund, Macau SAR

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Cited By

Li MZhou THuang ZYang JYang JGong C(2023)Dynamic Weighted Adversarial Learning for Semi-Supervised Classification under Intersectional Class MismatchACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363531020:4(1-24)Online publication date: 2-Dec-2023
https://dl.acm.org/doi/10.1145/3635310
Luo XJu WGu YQin YYi SWu DLiu LZhang M(2023)Toward Effective Semi-supervised Node Classification with Hybrid Curriculum Pseudo-labelingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362652820:3(1-19)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3626528
Mai SXing SHe JZeng YHu H(2022)Multimodal Graph for Unaligned Multimodal Sequence Analysis via Graph Convolution and Graph PoolingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/354292719:2(1-24)Online publication date: 9-Jun-2022
https://dl.acm.org/doi/10.1145/3542927

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