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Few-shot node classification via local adaptive discriminant structure learning |
Zhe XUE, Junping DU( ), Xin XU, Xiangbin LIU, Junfu WANG, Feifei KOU |
Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China |
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Abstract Node classification has a wide range of application scenarios such as citation analysis and social network analysis. In many real-world attributed networks, a large portion of classes only contain limited labeled nodes. Most of the existing node classification methods cannot be used for few-shot node classification. To train the model effectively and improve the robustness and reliability of the model with scarce labeled samples, in this paper, we propose a local adaptive discriminant structure learning (LADSL) method for few-shot node classification. LADSL aims to properly represent the nodes in the attributed graphs and learn a metric space with a strong discriminating power by reducing the intra-class variations and enlarging inter-class differences. Extensive experiments conducted on various attributed networks datasets demonstrate that LADSL is superior to the other methods on few-shot node classification task.
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Keywords
few-shot learning
node classification
graph neural network
adaptive structure learning
attention strategy
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Corresponding Author(s):
Junping DU
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Just Accepted Date: 16 February 2022
Issue Date: 02 August 2022
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