Diffusion induced graph representation learning
文献类型：期刊论文
| 作者 | Li, Fuzhen1,2; Zhu, Zhenfeng1,2; Zhang, Xingxing1,2; Cheng, Jian3 ; Zhao, Yao1,2
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2019-09-30 |
| 卷号 | 360页码:220-229 |
| 关键词 | Graph representation learning Graph embedding Diffusion model Auto-encoder Deep learning |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2019.06.012 |
| 通讯作者 | Zhu, Zhenfeng(zhfzhu@bjtu.edu.cn) |
| 英文摘要 | Nowadays, 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. |
| WOS关键词 | DIMENSIONALITY ; DISTRIBUTIONS |
| 资助项目 | National Key Research and Development of China[2016YFB0800404] ; National Natural Science Foundation of China[61572068] ; National Natural Science Foundation of China[61532005] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000480412700020 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Research and Development of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/27610]  |
| 专题 | 类脑芯片与系统研究 |
| 通讯作者 | Zhu, Zhenfeng |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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