CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算
Dynamic Graph Learning Convolutional Networks for Semi-supervised Classification
Fu, Sichao1; Liu, Weifeng1; Guan, Weili2; Zhou, Yicong3; Tao, Dapeng4; Xu, Changsheng5
Source PublicationACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
ISSN1551-6857
2021-04-01
Volume17Issue:1Pages:13
Corresponding AuthorLiu, Weifeng(liuwf@upc.edu.cn)
AbstractOver 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.
KeywordGraph representation learning graph convolutional networks semisupervised classification
DOI10.1145/3412846
Indexed BySCI
Language英语
Funding ProjectMajor Scientific and Technological Projects of CNPC[ZD2019-183008] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)[202000009] ; Science and Technology Development Fund, Macau SAR[189/2017/A3]
Funding OrganizationMajor Scientific and Technological Projects of CNPC ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; Science and Technology Development Fund, Macau SAR
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS IDWOS:000646396900004
PublisherASSOC COMPUTING MACHINERY
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44664
Collection模式识别国家重点实验室_多媒体计算
Corresponding AuthorLiu, Weifeng
Affiliation1.China Univ Petr East China, Coll Control Sci & Engn, Qingdao 266580, Peoples R China
2.Monash Univ, Fac Informat Technol, Clayton Campus, Melbourne, Vic, Australia
3.Univ Macau, Fac Sci & Technol, Macau 999078, Peoples R China
4.Yunnan Univ, Sch Informat Sci & Engn, Kunming 650091, Yunnan, Peoples R China
5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Fu, Sichao,Liu, Weifeng,Guan, Weili,et al. Dynamic Graph Learning Convolutional Networks for Semi-supervised Classification[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2021,17(1):13.
APA Fu, Sichao,Liu, Weifeng,Guan, Weili,Zhou, Yicong,Tao, Dapeng,&Xu, Changsheng.(2021).Dynamic Graph Learning Convolutional Networks for Semi-supervised Classification.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,17(1),13.
MLA Fu, Sichao,et al."Dynamic Graph Learning Convolutional Networks for Semi-supervised Classification".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 17.1(2021):13.
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