Graph Wavelet Convolutional Network with Graph Clustering

被引：0
|
作者：
Inatsuki, Hiroki [1 ]
Uto, Toshiyuki [1 ]
机构：
[1] Ehime Univ, Fac Engn, 3 Bunkyo Cho, Matsuyama, Ehime 7998577, Japan
来源：
2022 37TH INTERNATIONAL TECHNICAL CONFERENCE ON CIRCUITS/SYSTEMS, COMPUTERS AND COMMUNICATIONS (ITC-CSCC 2022) | 2022年
关键词：
Graph wavelet transform; Graph Convolutional Network (GCN); Graph clustering algorithm; Graph Wavelet Convolutional Network (GWCN);
D O I：
10.1109/ITC-CSCC55581.2022.989509
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we present a novel Graph Wavelet Convolutional Network (GWCN) approach with a graph clustering algorithm such as METIS. GWCN is a graph wavelet transform-based method. It has better locality than Graph Convolutional Network (GCN) using the graph Fourier transform, and results higher classification accuracy. In this work, the graph clustering algorithm is applied to GWCN for providing a mechanism to the mini-batch selection in deep learning, which has an effective impact on learning.
引用
收藏
页码：165 / 168
页数：4
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