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Location-aware convolutional neural networks for graph classification

文献类型：期刊论文

作者Wang, Zhaohui2,4; Cao, Qi4; Shen, Huawei1,2,4; Xu, Bingbing4; Cen, Keting2,4; Cheng, Xueqi2,3
刊名NEURAL NETWORKS
出版日期2022-11-01
卷号155页码:74-83
关键词Graph classification Convolutional neural networks Location-aware
ISSN号0893-6080
DOI10.1016/j.neunet.2022.07.035
英文摘要Graph patterns play a critical role in various graph classification tasks, e.g., chemical patterns often determine the properties of molecular graphs. Researchers devote themselves to adapting Convolutional Neural Networks (CNNs) to graph classification due to their powerful capability in pattern learning. The varying numbers of neighbor nodes and the lack of canonical order of nodes on graphs pose challenges in constructing receptive fields for CNNs. Existing methods generally follow a heuristic ranking-based framework, which constructs receptive fields by selecting a fixed number of nodes and dropping the others according to predetermined rules. However, such methods may lose important structure information through dropping nodes, and they also cannot learn task-oriented graph patterns. In this paper, we propose a Location learning-based Convolutional Neural Networks (LCNN) for graph classification. LCNN constructs receptive fields by learning the location of each node according to its embedding that contains structures and features information, then standard CNNs are applied to capture graph patterns. Such a location learning mechanism not only retains the information of all nodes, but also provides the ability for task-oriented pattern learning. Experimental results show the effectiveness of the proposed LCNN, and visualization results further illustrate the valid pattern learning ability of our method for graph classification. (c) 2022 Elsevier Ltd. All rights reserved.
资助项目National Natural Science Foundation of China[U21B2046] ; National Natural Science Foundation of China[62102402] ; National Key R&D Program of China[2020AAA0105200]
WOS研究方向Computer Science ; Neurosciences & Neurology
语种英语
WOS记录号WOS:000884681600005
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/19860]  
专题中国科学院计算技术研究所期刊论文
通讯作者Shen, Huawei
作者单位1.Beijing Acad Artificial Intelligence, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Data Intelligence Syst Res Ctr, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Zhaohui,Cao, Qi,Shen, Huawei,et al. Location-aware convolutional neural networks for graph classification[J]. NEURAL NETWORKS,2022,155:74-83.
APA Wang, Zhaohui,Cao, Qi,Shen, Huawei,Xu, Bingbing,Cen, Keting,&Cheng, Xueqi.(2022).Location-aware convolutional neural networks for graph classification.NEURAL NETWORKS,155,74-83.
MLA Wang, Zhaohui,et al."Location-aware convolutional neural networks for graph classification".NEURAL NETWORKS 155(2022):74-83.

入库方式： OAI收割

来源：计算技术研究所

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