# DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network
## 3 Virtual Model Lightweight Approach
### Method Main Process
For instance, GATv2 [63] introduces skip connections to enhance the model's expressive power and generalization performance, while OODGAT [64] incorporates a mechanism to detect and process out-of-distribution (OOD) nodes. CaGCN [65] improves the reliability of GCN by calibrating its confidence through post-processing steps. Experimental parameter settings were adopted from existing literature and optimized for practical requirements, with a maximum of 1000 training rounds and a learning rate of 0.001. Based on the experimental results in Reference [39], the k = 2, \(S_{1}\) = 25, \(S_{2}\) = \(\mathbf{10}\) were selected. Additionally, a betweenness centrality threshold of 0.03 was applied to filter out unimportant nodes during node selection. The experimental results are presented in Figure 4:1 The \(p\)-value, derived from a \(t\)-test, indicates that DTL-GNN outperforms traditional algorithms, achieving an accuracy of 95.4% on independent models and 96.3% on the overall model. While GAT and GATv2

Figure 4:2 The horizontal coordinates of the affinity matrix graph correspond to each part of the target model, and the vertical coordinates correspond to the parts contained in the database. Red nodes in the knowledge graph indicate highly relevant nodes, and blue indicates other nodes. show advantages in the attention mechanism for graph data, they encounter scalability issues when applied to large-scale mechanical models. OODGAT has made progress in detecting out-of-distribution (OOD) nodes, but its effectiveness is limited without such nodes. GCN suffers from over-smoothing, often leading to the loss of node-specific information. CaGCN enhances GCN's credibility through confidence calibration, but the improvement is marginal when applied to complex mechanical models. In contrast, DTL-GNN adaptively integrates multi-source embedding vectors using an adaptation function without additional preprocessing or calibration steps. Simultaneously, greater emphasis is placed on preserving topological information by embedding the structural attributes of the mechanical model within the node features. As a result, DTL-GNN substantially improves classification performance and adaptability when handling complex mechanical model graph data. DTL-GNN's performance with large-sample data slightly surpassed its performance with small-sample data. In the ablation study, we assessed the impact of critical components within the DTL-GNN. Removing Gattribute led to a significant accuracy drop to 70.9% for the overall model and 56.7% for the independent model, underscoring the critical role of Gattribute in the method's performance. In contrast, removing Gstruc resulted in a smaller decrease in accuracy, down to 94.8% and 93.3%, respectively, indicating that while Gstruc contributes to accuracy, its role is less crucial than that of Gattribute. This analysis emphasizes the importance of both components in maintaining the overall effectiveness of DTL-GNN. The results of error analysis, presented in Figure 11 and Table 4, reveal that the classification performance of Category 1 is significantly lower than that of other categories. This discrepancy is primarily due to the stricter requirements for mechanical parts in Category 1, coupled with a relatively small sample size. The confusion matrix further corroborates this, showing that Category 1 samples are frequently misclassified as Category 2, with correctly classified samples being a minority. A \(t\)-test on the features identified significant differences between the correctly and misclassified samples, particularly in the maximum centrality feature (\(p\)-value = 9.58 \(\times\) 10\({}^{-5}\)), which notably influences classification results. In contrast, the statistical analysis of other features, such as volume (\(p\)-value = 0.9586), revealed no significant differences, suggesting that these features contribute less to the model's classification performance.