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Code Vulnerability Detection Based on Graph Neural Network Cover
By: Yege Yang and  Guiping Li  
Open Access
|Jun 2025

Figures & Tables

Figure 1.

The Process of Code Standardization
The Process of Code Standardization

Figure 2.

The Process of Generating the PDG
The Process of Generating the PDG

Figure 3.

The Process of Slicing PDG
The Process of Slicing PDG

Figure 4.

The Process of Extracting Features from the Slice Graph
The Process of Extracting Features from the Slice Graph

Figure 5.

Vulnerability detection model architecture
Vulnerability detection model architecture

Figure 6.

Results of the Model Training in the Proposed Network
Results of the Model Training in the Proposed Network

Figure 7.

Ablation experiment
Ablation experiment

Figure 8.

Ablation experiment
Ablation experiment

Figure 9.

loss comparison
loss comparison

Figure 10.

Performance comparison of different models under evaluation
Performance comparison of different models under evaluation

TABLE TYPE STYLES

ParameterValue
Loss FunctionCrossEntropyLoss
Optimization AlgorithmAdam
Learning Rate0.0001
Weight Decay0.001
Batch Size16
Training Epochs500
Max Steps10000
Language: English
Page range: 62 - 73
Published on: Jun 16, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Yege Yang, Guiping Li, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.