A Graph Neural Network Node Classification Application Model with Enhanced Node Association
This study combines the present stage of the node classification problem with the fact that there is frequent noise in the graph structure of the graph convolution calculation, which can lead to the omission of some of the actual edge relations between nodes and the appearance of numerous isolated n...
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| Vydané v: | Applied sciences Ročník 13; číslo 12; s. 7150 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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01.06.2023
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| Abstract | This study combines the present stage of the node classification problem with the fact that there is frequent noise in the graph structure of the graph convolution calculation, which can lead to the omission of some of the actual edge relations between nodes and the appearance of numerous isolated nodes. In this paper, we propose the graph neural network model ENode-GAT for improving the accuracy of small sample node classification using the method of external referencing of similar word nodes, combined with Graph Convolutional Neural Network (GCN), Graph Attention Network (GAT), and the early stop algorithm. In order to demonstrate the applicability of the model, this paper employs two distinct types of node datasets for its investigations. The first is the Cora dataset, which is widely used in node classification at this time, and the second is a small-sample Stock dataset created by Eastern Fortune’s stock prospectus of the Science and Technology Board (STB). The experimental results demonstrate that the ENode-GAT model proposed in this paper obtains 85.1% classification accuracy on the Cora dataset and 85.3% classification accuracy on the Stock dataset, with certain classification advantages. It also verifies the future applicability of the model to the fields of stock classification, tender document classification, news classification, and government announcement classification, among others. |
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| AbstractList | This study combines the present stage of the node classification problem with the fact that there is frequent noise in the graph structure of the graph convolution calculation, which can lead to the omission of some of the actual edge relations between nodes and the appearance of numerous isolated nodes. In this paper, we propose the graph neural network model ENode-GAT for improving the accuracy of small sample node classification using the method of external referencing of similar word nodes, combined with Graph Convolutional Neural Network (GCN), Graph Attention Network (GAT), and the early stop algorithm. In order to demonstrate the applicability of the model, this paper employs two distinct types of node datasets for its investigations. The first is the Cora dataset, which is widely used in node classification at this time, and the second is a small-sample Stock dataset created by Eastern Fortune's stock prospectus of the Science and Technology Board (STB). The experimental results demonstrate that the ENode-GAT model proposed in this paper obtains 85.1% classification accuracy on the Cora dataset and 85.3% classification accuracy on the Stock dataset, with certain classification advantages. It also verifies the future applicability of the model to the fields of stock classification, tender document classification, news classification, and government announcement classification, among others. |
| Audience | Academic |
| Author | Zhang, Yuhang Zhang, Yu Xu, Yaoqun |
| Author_xml | – sequence: 1 givenname: Yuhang orcidid: 0000-0002-4751-2674 surname: Zhang fullname: Zhang, Yuhang – sequence: 2 givenname: Yaoqun orcidid: 0000-0002-5047-9350 surname: Xu fullname: Xu, Yaoqun – sequence: 3 givenname: Yu surname: Zhang fullname: Zhang, Yu |
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| Cites_doi | 10.1016/j.physa.2020.124289 10.1109/ACCESS.2023.3246525 10.2139/ssrn.4424703 10.1109/LGRS.2018.2869563 10.1016/j.jocs.2022.101695 10.24963/ijcai.2019/630 10.1109/TNNLS.2020.2978386 10.1109/ICCE-Taiwan55306.2022.9868975 10.3390/app13074614 10.1609/aaai.v32i1.11604 10.1109/TNN.2008.2005605 10.3390/app12147246 10.1016/j.knosys.2022.108538 10.1109/ACCESS.2023.3275085 10.1088/1742-6596/1994/1/012004 10.1109/CVPR.2019.00943 10.24963/ijcai.2019/369 10.1609/icwsm.v3i1.13979 |
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| SubjectTerms | Algorithms Classification Computational linguistics Datasets Deep learning graph attention network (GAT) graph convolutional neural network (GCN) graph neural network (GNN) Language processing Natural language interfaces Neural networks node classification Social networks |
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| Title | A Graph Neural Network Node Classification Application Model with Enhanced Node Association |
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