CASIA OpenIR
Diffusion induced graph representation learning
Li, Fuzhen1,2; Zhu, Zhenfeng1,2; Zhang, Xingxing1,2; Cheng, Jian3; Zhao, Yao1,2
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2019-09-30
Volume360Pages:220-229
Corresponding AuthorZhu, Zhenfeng(zhfzhu@bjtu.edu.cn)
AbstractNowadays, graph representation learning has aroused a lot of research interest, which aims to learn the latent low-dimensional representations of graph nodes, while preserving the graph structure. Based on the local smooth assumption, some existing methods have achieved significant success. However, although the structure information of data has been taken into consideration, these models fail to capture enough connectivity pattern such as high-order connections. To alleviate this issue, we propose a Graph Diffusion Network (GDN) that can dynamically preserve local and global consistency of graph. More specifically, Graph Diffusion Auto-encoder is utilized as the main framework in GDN to nonlinearly maintain global information volume. Different from simple auto-encoders, the forward propagation in our model is conducted through Graph Diffusion System which can guide the random walk of information flow to sense the high-order local relationships on graph. Furthermore, to discover a customized graph structure that reveals the similarities between nodes, the connection relationship between nodes are refined by learned metrics with the preservation of scale-free property. By the dynamically self-refining on the graph structure, it can be promoted towards learning the intrinsic node representations in a progressive way. Experimental results on node classification tasks demonstrate the effectiveness of the proposed GDN model. (C) 2019 Elsevier B.V. All rights reserved.
KeywordGraph representation learning Graph embedding Diffusion model Auto-encoder Deep learning
DOI10.1016/j.neucom.2019.06.012
WOS KeywordDIMENSIONALITY ; DISTRIBUTIONS
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development of China[2016YFB0800404] ; National Natural Science Foundation of China[61572068] ; National Natural Science Foundation of China[61532005]
Funding OrganizationNational Key Research and Development of China ; National Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000480412700020
PublisherELSEVIER
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27611
Collection中国科学院自动化研究所
Corresponding AuthorZhu, Zhenfeng
Affiliation1.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China
2.Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Fuzhen,Zhu, Zhenfeng,Zhang, Xingxing,et al. Diffusion induced graph representation learning[J]. NEUROCOMPUTING,2019,360:220-229.
APA Li, Fuzhen,Zhu, Zhenfeng,Zhang, Xingxing,Cheng, Jian,&Zhao, Yao.(2019).Diffusion induced graph representation learning.NEUROCOMPUTING,360,220-229.
MLA Li, Fuzhen,et al."Diffusion induced graph representation learning".NEUROCOMPUTING 360(2019):220-229.
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