图自适应原型网络的小样本节点分类方法

doi:10.16451/j.cnki.issn1003-6059.202208005

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Abstract Few-shot node classification aims to make machines recognize and classify quickly from a small number of nodes. Existing few-shot node classification models are easily affected by the inaccurate node features extracted by encoders and the intra-class outliers of query set instances in sub-tasks. Therefore, a graph adaptive prototypical networks(GAPN) model is proposed. Firstly, the nodes are embedded into the metric space by the graph encoder. Then, prototypes are computed by fusing the global importance and the local importance as weight of support set instances, and thus more robust prototypes can be learned adaptively for query set instances. Finally, the distance between the class prototypes of the adaptive task and the query set instance is calculated to generate the classification probability. By minimizing the positive marginal feedback loss between the classification probability and the true label, network parameters are updated backward and more discriminative node features can be learned. Experimental results on common graph datasets show that GAPN model yields better node classification performance.

Key words： Few Shot Learning Node Classification Graph Neural Networks Prototypical Networks

Received: 21 March 2022

ZTFLH:

TP391

Fund:National Natural Science Foundation of China(No.61976184,61772323)

Corresponding Authors: WEI Wei, Ph.D., professor. His research interests include data mining and machine learning.

About author:: GUO Ruize, master student. Her research interests include machine learning.
CUI Junbiao, Ph.D. candidate. His research interests include data mining and machine learning.
FENG Kai, Ph.D., associate professor. His research interests include interconnection network and graph.

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	GUO Ruize
	WEI Wei
	CUI Junbiao
	FENG Kai

Cite this article:

GUO Ruize,WEI Wei,CUI Junbiao等. Few-Shot Node Classification Method of Graph Adaptive Prototypical Networks[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(8): 743-753.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202208005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2022/V35/I8/743

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