# DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network
## 3 Virtual Model Lightweight Approach
### Method Main Process
The combined performance of DTL-GNN and other algorithms is shown in Figure 4:3 Micro averaging and macro averaging are key indicators for evaluating the performance of classification models, where micro averaging focuses on overall performance and macro averaging focuses on the average performance of each category, especially for imbalanced datasets. The micro average F1 score, micro average recall, and micro average precision of DTL-GNN are 97.9%, 98.2%, and 97.8%, respectively. Additionally, the macro average performance metrics for DTL-GNN are 70% for F1 score, 69% for recall, and 74% for \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **GAT [61]** & **GATv2 [63]** & \begin{tabular}{c} **OODCAT** \\ [64] \\ \end{tabular} & **GCN [62]** & **CaGCN [65]** & **DTL-GNN** & \begin{tabular}{c} **DTL-GNN-** \\ **Gattribute** \\ \end{tabular} & \begin{tabular}{c} **DTL-GNN-** \\ **Gastric** \\ \end{tabular} \\ \hline \begin{tabular}{c} Overall \\ (model) \\ (factor) \\ (\(t\)-test) \\ \end{tabular} & 69.8\(\backslash\)66.5\(\backslash\)69.3 & 71.4\(\backslash\)70.7\(\backslash\)72.2 & 72.9\(\backslash\)76.2\(\backslash\)71.0 & 67.4\(\backslash\)69.2\(\backslash\)65.2 & 65.1\(\backslash\)70.2\(\backslash\)69.9 & 93.2\(\backslash\)96.3\(\backslash\)94.5 & 71.3\(\backslash\)70.9\(\backslash\)66.2 & 89.7\(\backslash\)94.8\(\backslash\)93.1 \\ \begin{tabular}{c} (\(t\)-value) \\ (\(t\)-test) \\ \end{tabular} & 4.38 \(\times\) 10\({}^{-5}\) & 2.02\(\times\) 10\({}^{-5}\) & 2.7\(\times\) 10\({}^{-5}\) & 4.81\(\times\) 10\({}^{-5}\) & 1.53\(\times\) 10\({}^{-5}\) & – & – & – \\ \hline \
\begin{tabular}{c} Independent \\ (model) \\ (robotic arm) \\ \end{tabular} & 42.5\(\backslash\)40.0\(\backslash\)37.8 & 50.0\(\backslash\)66.7\(\backslash\)56.3 & 40.1\(\backslash\)50.0\(\backslash\)33.3 & 40.0\(\backslash\)33.3\(\backslash\)38.4 & 60.7\(\backslash\)55.4\(\backslash\)67.2 & 95.0\(\backslash\)91.6\(\backslash\)95.4 & 56.7\(\backslash\)62.1\(\backslash\)53.3 & 91.0\(\backslash\)93.3\(\backslash\)90.6 \\ \hline
\begin{tabular}{c} \(p\)-value \\ (\(t\)-test) \\ \end{tabular} & 7.67\(\times\) 10\({}^{-6}\) & 1.928\(\times\) 10\({}^{-4}\) & 4.51\(\times\) 10\({}^{-5}\) & 1.75\(\times\) 10\({}^{-5}\) & 8.12\(\times\) 10\({}^{-5}\) & – & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of classification accuracy using different algorithms on two types of models. precision. This experiment found that due to the difficulty of recognizing certain parts with fewer features, the predictive performance is poorer, which leads to the micro average index being significantly better than the macro average index. DTL-GNN recognizes such confusing parts better than other algorithms, and compared with other algorithms, the macro average index is improved by 37% to 40%, and the micro average index is higher than that of other algorithms by 10% to 39%, respectively. To verify the effect of DTL-GNN on model simplification, we measured it based on the two previously mentioned dimensions: the model file size and detail retention, as shown in Table 5. First, the DTL-GNN method retains 11.0% of the surface features in decorative, electrical, and infrastructure parts, while the file size is reduced by 64.3%. Second, when simplifying essential parts such as complex surfaces and locati \begin{table}
\begin{tabular}{c c c} \hline \hline  & **T-Statistic** & \(\mathbf{p}\)**-Value** \\ \hline Degrees centricity & \(1.5674\) & \(0.1171\) \\ Eigenvectors centricity & \(0.5487\) & \(0.5832\) \\ Biggest degree & \(-1.2331\) & \(0.2176\) \\ Maxi degree centrality & \(3.3046\) & \(9.58\times 10^{-5}\) \\ Dot number & \(-1.4595\) & \(0.1445\) \\ Line number & \(-1.4595\) & \(0.1445\) \\ Surface number & \(-1.6998\) & \(0.0892\) \\ Volume & \(0.0519\) & \(0.9586\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of features. Figure 11: Confusion matrix for the wrong samples.