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Node classification across networks via category-level domain adaptive network embedding

文献类型：期刊论文

作者Shi, Boshen1,4; Wang, Yongqing4; Shao, Jiangli1,4; Shen, Huawei4; Li, Yangyang2,3; Cheng, Xueqi4
刊名KNOWLEDGE AND INFORMATION SYSTEMS
出版日期2023-07-30
页码24
关键词Node classification across networks Graph neural networks Domain adaptation Transfer learning
ISSN号0219-1377
DOI10.1007/s10115-023-01942-2
英文摘要To improve the performance of classifying nodes on unlabeled or scarcely-labeled networks, the task of node classification across networks is proposed for transferring knowledge from similar networks with rich labels. As data distribution shift exists across networks, domain adaptive network embedding is proposed to overcome such challenge by learning network-invariant and discriminative node embeddings, in which domain adaptation technique is applied to network embedding for reducing domain discrepancy. However, existing works merely discuss category-level domain discrepancy which is crucial to better adaptation and classification. In this paper, we propose category-level domain adaptive network embedding. The key idea is minimizing intra-class domain discrepancy and maximizing inter-class domain discrepancy between source and target networks simultaneously. To further enhance classification performance on target network, we reduce embedding variation inside each class and enlarge it between different classes. Graph attention network is adopted for learning network embeddings. In addition, a novel pseudo-labeling strategy for target network is developed to better compute category-level information. Theoretical analysis guarantees the effectiveness of our model. Furthermore, extensive experiments on real-world datasets show that our model achieves the state-of-art performance, in particular, outperforming existing domain adaptive network embedding models by up to 32%.
资助项目National Natural Science Foundation of China[U21B2046] ; China Postdoctoral Science Foundation[2022M713206]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001039402200001
出版者SPRINGER LONDON LTD
源URL[http://119.78.100.204/handle/2XEOYT63/21287]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Shi, Boshen; Wang, Yongqing
作者单位1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
2.CAEIT, Natl Engn Res Ctr Risk Percept & Prevent NEL RPP, Beijing 100041, Peoples R China
3.Acad Cyber, Beijing 100085, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Data Intelligence Syst Res Ctr, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Shi, Boshen,Wang, Yongqing,Shao, Jiangli,et al. Node classification across networks via category-level domain adaptive network embedding[J]. KNOWLEDGE AND INFORMATION SYSTEMS,2023:24.
APA Shi, Boshen,Wang, Yongqing,Shao, Jiangli,Shen, Huawei,Li, Yangyang,&Cheng, Xueqi.(2023).Node classification across networks via category-level domain adaptive network embedding.KNOWLEDGE AND INFORMATION SYSTEMS,24.
MLA Shi, Boshen,et al."Node classification across networks via category-level domain adaptive network embedding".KNOWLEDGE AND INFORMATION SYSTEMS (2023):24.

入库方式： OAI收割

来源：计算技术研究所

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