Node-Level Adaptive Graph Convolutional Neural Network for Node Classification Tasks

被引：0
|
作者：
Wang X. [1 ]
Hu R. [1 ]
Guo Y. [1 ]
Du H. [1 ]
Zhang B. [3 ]
Wang W. [2 ,3 ]
机构：
[1] School of Computer and Information Technology, Shanxi University, Taiyuan
[2] Key Laboratory of Computational Intelligence and Chinese Information Processing, Ministry of Education, Shanxi University, Taiyuan
[3] Department of Network Security, Shanxi Police College, Taiyuan
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2024年 / 37卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Adaptive Aggregation; Adaptive Sampling; Graph Neural Networks (GNNs); Node Classification; Spectral Graph Theory;
D O I：
10.16451/j.cnki.issn1003-6059.202404001
中图分类号：
学科分类号：
摘要：
Graph neural networks learn node embeddings by recursively sampling and aggregating information from nodes in a graph. However, the relatively fixed pattern of existing methods in node sampling and aggregation results in inadequate capture of local pattern diversity, thereby degrading the performance of the model. To solve this problem, a node-level adaptive graph convolutional neural network(NA-GCN) is proposed. A sampling strategy based on node importance is designed to adaptively determine the neighborhood size of each node. An aggregation strategy based on the self-attention mechanism is presented to adaptively fuse the node information within a given neighborhood. Experimental results on multiple benchmark graph datasets show the superiority of NA-GCN in node classification tasks. © 2024 Science Press. All rights reserved.
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收藏
页码：287 / 298
页数：11
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