Learning Network-Invariant and Label-Discriminative Representations for Cross-Network Node Classification
Yang, Linyao1,2; Xu, Yancai1; Hou, Jiachen3,4; Dai, Yuxin5; Lv, Chen6
Conference Name2021 China Automation Congress (CAC)
Source Publication2021 China Automation Congress (CAC)
Conference Date2021-10-22
Conference PlaceBeijing
Author of SourceIEEE
Publication PlaceBeijing

Networks are ubiquitous data structures in the real world. The accurate and efficient analysis of networks is critical to realizing many intelligent network-based services. However, most existing network analysis methods are developed for single networks and require a lot of labeled data, which is costly and time-consuming to acquire. Transfer learning has been widely accepted as an effective paradigm for tackling this problem by reusing the model trained on a supervised task. However, transfer learning on the non-euclidean network data has been investigated by no more than a few studies. To realize accurate node classification based on the knowledge learned from the labeled source network, this paper proposes to learn network-invariant and label-discriminative representations based on graph embedding and linear discriminant analysis. Specifically, we embed the source and target networks into adjacent vector spaces based on the graph attention network by minimizing the Sinkhorn distributional distances between their embeddings. To obtain label-discriminative features for learning better classification models, we then utilize a transferable linear discriminative analysis method to project the embeddings into label-discriminative subspaces. In the end, a support vector machine model trained on the labeled source network is utilized to classify the target nodes. Experiments on two pairs of networks illustrate that our method achieves the best performance and evaluates the effectiveness of the proposed modules.

KeywordTransfer learning node classification Sinkhorn distance linear discriminative analysis
MOST Discipline Catalogue工学::计算机科学与技术(可授工学、理学学位)
Indexed ByEI
Citation statistics
Document Type会议论文
Affiliation1.The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Qingdao Academy of Intelligent Industries
4.Institute of Systems Engineering, Macau University of Science and Technology
5.School of Electrical Engineering and Automation, Wuhan University
6.China Electric Power Research Institute
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yang, Linyao,Xu, Yancai,Hou, Jiachen,et al. Learning Network-Invariant and Label-Discriminative Representations for Cross-Network Node Classification[C]//IEEE. Beijing:IEEE,2021:5075-5080.
Files in This Item: Download All
File Name/Size DocType Version Access License
Learning_Network-Inv(910KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yang, Linyao]'s Articles
[Xu, Yancai]'s Articles
[Hou, Jiachen]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yang, Linyao]'s Articles
[Xu, Yancai]'s Articles
[Hou, Jiachen]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yang, Linyao]'s Articles
[Xu, Yancai]'s Articles
[Hou, Jiachen]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Learning_Network-Invariant_and_Label-Discriminative_Representations_for_Cross-Network_Node_Classification.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.