## 4 Methodology
### Graph Convolutional Neural Network Module
#### 5.3.4 Model Performance Analysis

In order to verify the training performance of this paper's model under different data volumes, four datasets were selected in this paper, with 1000, 2000, 3000, and 5000 nodes, respectively. As shown in Figure 13, in datasets 1 and 2, the training loss values and accuracy rates of the two datasets change dramatically when the epoch is less than 50, and show a small range of fluctuation after 50 epochs, at which point the training accuracy tends to increase and training loss value tends to decrease. When the epoch is between 300 and 400, the accuracy fluctuates slightly at around 0.97-0.98, and the loss value fluctuates slightly at around 0.13, and then stabilizes. In datasets 3 and 4, the training loss values and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Acc (\%)** & **Pre (\%)** & **Rec (\%)** & **F1** \\ \hline SVM & 79.51 & 78.52 & 79.51 & 78.72 \\ XGBost & 82.36 & 81.92 & 82.36 & 81.19 \\ MLP & 84.43 & 84.67 & 84.43 & 81.74 \\ GAT & 80.43 & 71.43 & 69.46. & 69.94 \\ GraphSAGE & 95.76 & 92.49 & 92.19 & 92.32 \\ GCN-LSTM & 98.62 & 97.61 & 97.37 & 97.35 \\ GCN-GRU-Attention & 99.61 & 99.30 & 99.26 & 99.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**GCN** & **GRU** & **Attention** & **Acc (\%)** & **Pre (\%)** & **Rec (\%)** & **F1** \\ \hline \(\surd\) & \(\times\) & \(\times\) & 87.12 & 88.24 & 87.12 & 86.59 \\ \(\times\) & \(\surd\) & \(\times\) & 81.99 & 81.60 & 81.99 & 80.47 \\ \(\surd\) & \(\surd\) & \(\times\) & 93.44 & 92.00 & 85.80 & 83.99 \\ \(\surd\) & \(\surd\) & \(\surd\) & 99.61 & 99.30 & 99.26 & 99.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model ablation experiment performance table. accuracy of both datasets change drastically when the epoch is less than 50, and fluctuate more dramatically when the epoch is in the interval range f rom50 to 200, but fluctuate less after the epoch is greater than 200. When the epoch is between 300 and 400, the accuracy fluctuates around 98%, and the loss value fluctuates around 0.12 and then also stabilizes around 400. This shows that the trained network is able to adapt to new unseen data with the same distribution as the training data, and that the size of the dataset does not have a significant effect on the convergence of the loss values of the model and the accuracy of the model results. In order to further verify the changes in each evaluation metric (Acc(%), Pre(%), Rec(%), F1) of the model under different data volumes, as shown in Figure 14, the horizontal coordinate is the number of different nodes and the vertical coordinate is the value of the evaluation metrics. Overall, as the data volume increases, the value of each indicator gradually increases, and finally each value reaches the optimum when the number of nodes is 5000. Although the size of the data volume has an impact on the performance of the model, the impact is not large. When 1000 nodes are used in small datasets, in this paper's model with an Acc value of about 96%, the lower F1 value is also in the range of 89% or so, when 5000 are used in a large dataset, the model of the value of each evaluation index is in the range of 99% or so. In summary, the effectiveness of this paper's algorithmic model in terms of spatio-temporal data is proved, and the robustness of retrieval is guaranteed. Additionally, the model has a good generalization ability. Figure 13: Training performance of the model: (**a**) training performance with 1000 nodes, (**b**) training performance with 2000 nodes, (**c**) training performance with 3000 nodes, (**d**) training performance with 5000 nodes. ## 6 Conclusions

Dynamic graph anomaly detection has a relatively important research status and practical significance. For the first time, we introduced gantry transaction data for gantry anomaly detection, an innovative approach that allows for us to comprehensively assess gantry anomalies.