## 5 Experiments
### Experimental Results  
We tested the multilayer perceptron (MLP), the classical Graph Neural Network models, GCN and GAT, and the currently popular GDC for comparison and tuned the reference model as best as possible according to the optimal experimental setups provided by references, trained and tested on four datasets, and saved the optimal model parameters after 200 rounds of training; on the test set, respectively, 10 tests were performed and then averaged. The accuracy and \(F1\) scores of each model are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **Methods** & **S06\_gear** & **S06\_cylinder** & **S06\_shaft** & **S06\_piston** \\ \hline \multirow{8}{*}{Acc} & MLP & 82.23 \(\pm\) 0.52 & 82.99 \(\pm\) 0.38 & 83.52 \(\pm\) 0.42 & 84.09 \(\pm\) 0.33 \\  & GCN & 90.29 \(\pm\) 0.72 & 90.09 \(\pm\) 0.29 & 90.52 \(\pm\) 0.28 & 91.11 \(\pm\) 0.11 \\  & GAT & 90.79 \(\pm\) 0.12 & 91.50 \(\pm\) 0.45 & 91.93 \(\pm\) 0.19 & 92.35 \(\pm\) 0.07 \\ \cline{2-6}  & GraphSAGE & 91.28 \(\pm\) 0.20 & 91.58 \(\pm\) 0.34 & 92.01 \(\pm\) 0.43 & 92.63 \(\pm\) 0.23 \\ \cline{2-6}  & GraphSAINT & 91.35 \(\pm\) 0.13 & 92.15 \(\pm\) 0.18 & 91.98 \(\pm\) 0.67 & 92.57 \(\pm\) 0.64 \\ \cline{2-6}  & GDC & 92.86 \(\pm\) 0.12 & 92.60 \(\pm\) 0.46 & 93.03 \(\pm\) 0.51 & 93.63 \(\pm\) 0.38 \\ \cline{2-6}  & DiffusAL & 92.95 \(\pm\) 0.32 & 92.64 \(\pm\) 0.33 & 93.07 \(\pm\) 0.19 & 93.69 \(\pm\) 0.26 \\ \cline{2-6}  & GraphDHNA & 94.99 \(\pm\) 0.25 & 94.83 \(\pm\) 0.31 & 94.32 \(\pm\) 0.18 & 96.13 \(\pm\) 0.24 \\ \hline \multirow{8}{*}{F1} & MLP & 80.29 \(\pm\) 0.63 & 80.09 \(\pm\) 0.41 & 81.52 \(\pm\) 0.25 & 82.11 \(\pm\) 0.24 \\  & GCN & 86.67 \(\pm\) 0.72 & 87.82 \(\pm\) 0.67 & 86.36 \(\pm\) 0.74 & 87.32 \(\pm\) 0.53 \\ \cline{1-1}  & GAT & 87.76 \(\pm\) 0.46 & 87.91 \(\pm\) 0.54 & 86.45 \(\pm\) 0.33 & 88.41 \(\pm\) 0.51 \\ \cline{1-1} \cline{2-6}  & GraphSAGE & 88.89 \(\pm\) 0.60 & 89.04 \(\pm\) 0.48 & 87.58 \(\pm\) 0.46 & 89.54 \(\pm\) 0.46 \\ \cline{1-1} \cline{2-6}  & GraphSAINT & 87.07 \(\pm\) 0.39 & 89.22 \(\pm\) 0.53 & 88.76 \(\pm\) 0.64 & 89.72 \(\pm\) 0.44 \\ \cline{1-1} \cline{2-6}  & GDC & 89.54 \(\pm\) 0.29 & 90.69 \(\pm\) 0.19 & 89.23 \(\pm\) 0.51 & 90.19 \(\pm\) 0.49 \\ \cline{1-1} \cline{2-6}  & DiffusAL & 90.35 \(\pm\) 0.48 & 91.23 \(\pm\) 0.35 & 90.04 \(\pm\) 0.61 & 91.39 \(\pm\) 0.42 \\ \cline{1-1} \cline{2-6}  & GraphDHNA & 92.69 \(\pm\) 0.36 & 92.13 \(\pm\) 0.24 & 92.41 \(\pm\) 0.16 & 94.38 \(\pm\) 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results.  
Figure 3: GraphDHNA’s loss variation on four training datasets. (**a**) Training on S06\_gear; (**b**) training on S06\_cylinder; (**c**) training on S06\_shaft; (**d**) training on S06\_piston.  
As seen from the experimental results in Table 3, the simple MLP method performs poorly, reaching only 84% classification accuracy; the classical GNN methods, such as GCN and GAT, intersect MLP with a sizable improvement, reaching the 90% threshold; the graph sampling method further obtains an improvement of about 10%, reaching 91-92%. The graph diffusion GDC and DiffusionAL models achieved a more significant advantage among all the baseline models, reaching a classification accuracy of 93%. GraphDHNA achieved the highest scores in both classification accuracy and \(F1\) scores, reaching 96% and 94, which surfaces the superiority of GraphDHNA in dealing with machining center anomaly detection tasks.