Learning graph structure via graph convolutional networks
Zhang, Qi1,2; Chang, Jianlong1,2; Meng, Gaofeng1; Xu, Shibiao1; Xiang, Shiming1,2; Pan, Chunhong1
发表期刊PATTERN RECOGNITION
ISSN0031-3203
2019-11-01
卷号95期号:-页码:308-318
摘要

Graph convolutional neural networks have aroused more and more attentions on account of the ability to handle the graph-structured data defined on irregular or non-Euclidean domains. Different from the data defined on regular grids, each node in the graph-structured data has different number of neighbors, and the interactions and correlations between nodes vary at different locations, resulting in complex graph structure. However, the existing graph convolutional neural networks generally pay little attention to exploiting the graph structure information. Moreover, most existing graph convolutional neural networks employ the weight sharing strategy which lies on the statistical assumption of stationarity. This assumption is not always verified on the graph-structured data. To address these issues, we propose a method that learns Graph Structure via graph Convolutional Networks (GSCN), which introduces the graph structure parameters measuring the correlation degrees of adjacent nodes. The graph structure parameters are constantly modified the graph structure during the training phase and will help the filters of the proposed method to focus on the relevant nodes in each neighborhood. Meanwhile by combining the graph structure parameters and kernel weights, our method, which relaxes the restriction of weight sharing, is better to handle the graph-structured data of non-stationarity. In addition, the non-linear activation function ReLU and the sparse constraint are employed on the graph structure parameters to promote GSCN to focus on the important links and filter out the insignificant links in each neighborhood. Experiments on various tasks, including text categorization, molecular activity detection, traffic forecasting and skeleton-based action recognition, illustrate the validity of our method. (C) 2019 Elsevier Ltd. All rights reserved.

关键词Deep learning Graph convolutional neural networks Graph structure learning Changeable kernel sizes
DOI10.1016/j.patcog.2019.06.012
关键词[WOS]NEURAL-NETWORK
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61573352] ; National Natural Science Foundation of China[61773377] ; National Natural Science Foundation of China[61773377] ; National Natural Science Foundation of China[61573352] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61620106003]
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000478710600026
出版者ELSEVIER SCI LTD
七大方向——子方向分类机器学习
引用统计
被引频次:32[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/27751
专题模式识别国家重点实验室_先进时空数据分析与学习
通讯作者Meng, Gaofeng
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位模式识别国家重点实验室
通讯作者单位模式识别国家重点实验室
推荐引用方式
GB/T 7714
Zhang, Qi,Chang, Jianlong,Meng, Gaofeng,et al. Learning graph structure via graph convolutional networks[J]. PATTERN RECOGNITION,2019,95(-):308-318.
APA Zhang, Qi,Chang, Jianlong,Meng, Gaofeng,Xu, Shibiao,Xiang, Shiming,&Pan, Chunhong.(2019).Learning graph structure via graph convolutional networks.PATTERN RECOGNITION,95(-),308-318.
MLA Zhang, Qi,et al."Learning graph structure via graph convolutional networks".PATTERN RECOGNITION 95.-(2019):308-318.
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