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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (10): 93-102    DOI: 10.11925/infotech.2096-3467.2022.0071
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Classification Model for Scholarly Articles Based on Improved Graph Neural Network
Huang Xuejian1,2,Liu Yuyang3,Ma Tinghuai1()
1College of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
2VR College of Modern Industry, Jiangxi University of Finance and Economics, Nanchang 330013, China
3College of Humanities, Jiangxi University of Finance and Economics, Nanchang 330013, China
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Abstract  

[Objective] This paper tries to address the over-smoothing issues of the traditional graph neural network, and then realizes the weight adaptive allocation of different depths and neighbors, aiming to improve the performance of academic literature classification. [Methods] We proposed an improved graph neural network model for academic paper classification. First, with the help of multi-head attention mechanism, the new model learned a variety of related features among documents, and adaptively distributing the weights of different neighbor nodes. Then, based on the residual network structure, the model aggregated outputs of each layer node, and provided the learning of adaptive aggregation radius. Finally, with the help of improved graph neural network, the model learned feature representation of each node in the paper citation graph, which was input into the multi-layer fully connected network to obtain the final classification. [Results] We examined our model on large-scale real datasets. The accuracy of our model reached 0.61, which is 0.04 and 0.14 higher than those of the GCN and Transformer models. [Limitations] More research is needed to improve the classification accuracy of small categories and difficult to distinguish samples. [Conclusions] The improved graph neural network can effectively conduct classification for academic articles.

Key wordsGraph Neural Network      Attention Mechanism      Residual Network      Deep Learning      Paper Classification      Text Classification     
Received: 23 January 2022      Published: 16 November 2022
ZTFLH:  G202 TP319  
Fund:National Key R&D Program of China(2021YFE0104400);Jiangxi Provincial Humanities and Social Sciences Research Project(JY21253);Youth Project of the 14th Five Year Plan of Jiangxi Educational Science(21QN012)
Corresponding Authors: Ma Tinghuai,ORCID:0000-0003-2320-1692      E-mail: thma@nuist.edu.cn

Cite this article:

Huang Xuejian, Liu Yuyang, Ma Tinghuai. Classification Model for Scholarly Articles Based on Improved Graph Neural Network. Data Analysis and Knowledge Discovery, 2022, 6(10): 93-102.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0071     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I10/93

Model Architecture
Subgraph Sampling Process
Aggregation Based on Residual Structure
类型 数量
节点总数 3 063 061
边总数 29 168 650
孤立节点数 535 347
有标签节点数 1 044 417
非标签节点数 2 018 644
节点类别数 23
最大入度 15 979
平均入度 13.6
最大出度 9 102
平均出度 10.5
Statistical Analysis of Data Sets
Sample Category Distribution
参数类 参数名称 参数值
模型参数 采样邻居阶数 3
每层最大采样邻居数量 10,10,10
图神经网络隐含层维度 256
注意力机制随机置零概率 0.1
注意力个数 3
全连接层数 5
全连接层隐含层维度 512,256,128,64,23
全连接层随机置零概率 0.1
训练参数 学习率 0.001
正则化参数 1e-6
最大迭代次数 100
早停等待次数 20
批量训练样本数 1024
Main Parameter Setting
Experimental Comparison of Different Graph Neural Networks at Different Aggregation Radius
Experimental Comparison of ResGAT Under Different Attention Counts
Experimental Comparison with Supervised Text Classification Models
Error Visualization
类别 占比 查准率 查全率 F1值
A 0.3% 0.00 0.00 0.00
B 6.2% 0.61 0.75 0.68
C 10.3% 0.74 0.71 0.72
D 10.3% 0.65 0.67 0.66
E 4.6% 0.44 0.73 0.55
F 3.3% 0.60 0.73 0.66
G 4.1% 0.62 0.51 0.56
H 6.6% 0.59 0.32 0.42
I 2.1% 0.73 0.68 0.71
J 2.3% 0.33 0.10 0.16
K 3.1% 0.66 0.44 0.52
L 5.2% 0.57 0.66 0.61
M 8.3% 0.52 0.56 0.54
N 9.7% 0.81 0.80 0.80
O 1.8% 0.35 0.35 0.35
P 4.9% 0.65 0.52 0.57
Q 2.0% 0.73 0.76 0.74
R 3.1% 0.53 0.53 0.53
S 2.2% 0.72 0.94 0.81
T 2.1% 0.57 0.77 0.66
U 2.3% 0.51 0.49 0.50
V 4.0% 0.39 0.30 0.34
W 1.3% 0.51 0.34 0.41
Classification Results of Different Classes
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