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PKGCN: prior knowledge enhanced graph convolutional network for graph-based semi-supervised learning | |
Yu, Shaowei1,2![]() ![]() ![]() | |
发表期刊 | INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
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ISSN | 1868-8071 |
2019-11-01 | |
卷号 | 10期号:11页码:3115-3127 |
摘要 | Graph is a widely existed data structure in many real world scenarios, such as social networks, citation networks and knowledge graphs. Recently, Graph Convolutional Network (GCN) has been proposed as a powerful method for graph-based semi-supervised learning, which has the similar operation and structure as Convolutional Neural Networks (CNNs). However, like many CNNs, it is often necessary to go through a lot of laborious experiments to determine the appropriate network structure and parameter settings. Fully exploiting and utilizing the prior knowledge that nearby nodes have the same labels in graph-based neural network is still a challenge. In this paper, we propose a model which utilizes the prior knowledge on graph to enhance GCN. To be specific, we decompose the objective function of semi-supervised learning on graphs into a supervised term and an unsupervised term. For the unsupervised term, we present the concept of local inconsistency and devise a loss term to describe the property in graphs. The supervised term captures the information from the labeled data while the proposed unsupervised term captures the relationships among both labeled data and unlabeled data. Combining supervised term and unsupervised term, our proposed model includes more intrinsic properties of graph-structured data and improves the GCN model with no increase in time complexity. Experiments on three node classification benchmarks show that our proposed model is superior to GCN and seven existing graph-based semi-supervised learning methods. |
关键词 | Graph convolutional network Semi-supervised learning Prior knowledge Node classification |
DOI | 10.1007/s13042-019-01003-7 |
关键词[WOS] | PERFORMANCE |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61532006] ; Beijing Municipal Natural Science Foundation[4172063] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[U1636220] ; Beijing Municipal Natural Science Foundation[4172063] ; National Natural Science Foundation of China[61532006] |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000494802500009 |
出版者 | SPRINGER HEIDELBERG |
七大方向——子方向分类 | 机器学习 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/28867 |
专题 | 多模态人工智能系统全国重点实验室_人工智能与机器学习(杨雪冰)-技术团队 |
通讯作者 | Yu, Shaowei |
作者单位 | 1.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yu, Shaowei,Yang, Xuebing,Zhang, Wensheng. PKGCN: prior knowledge enhanced graph convolutional network for graph-based semi-supervised learning[J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,2019,10(11):3115-3127. |
APA | Yu, Shaowei,Yang, Xuebing,&Zhang, Wensheng.(2019).PKGCN: prior knowledge enhanced graph convolutional network for graph-based semi-supervised learning.INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS,10(11),3115-3127. |
MLA | Yu, Shaowei,et al."PKGCN: prior knowledge enhanced graph convolutional network for graph-based semi-supervised learning".INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 10.11(2019):3115-3127. |
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