## 3 Proposed Model
### Experiment Results and Discussion  
Four models--AKNN-GCN, AKNN-DMGCN, BAKNN-GCN, and BAKNN-DMGCN--were run on the four datasets, and the weight coefficient \(\gamma\) in the AKNN method was 1.75. Table 2 presents the average accuracy of the four models on the four datasets. As displayed in the table, the accuracy of AKNN-GCN on the four datasets is 1.46%, 2.4%, 3.03%, and 1.12% higher than that of BAKNN-GCN, respectively. Furthermore, the accuracy of AKNN-DMGCN on the four datasets is 1.17%, 3.64%, 2.99%, and 2.6% higher than that of BAKNN-DMGCN, respectively. It can be seen that the improved AKNN method greatly improves the overall performance of the model. This is because the model learns the characteristics of the data from the previously constructed graph; therefore, the information contained in the constructed graph directly affects the overall performance of the model. In addition, the improved AKNN method improves the accuracy of DMGCN more than the accuracy of the GCN, which also demonstrates that the learning ability of DMGCN is stronger than that of the GCN. The two most representative sets--dataset1 and dataset4--were selected, and Figures 7 and 8 present the confusion matrices of the four models under dataset1 and dataset4, respectively. The vertical axis represents the actual label, and the horizontal axis represents the predicted label. If the abscissa value is equal to the ordinate value, the diagnosis is correct, that is, the diagonal line represents the accuracy. What needs to be emphasized here is that the percentage in each cell represents the accuracy of a category, that is, the sum of each row in the confusion matrix should be 100%. The BAKNN method was used to construct the graph for dataset1 and dataset4. Figures 9 and 10 present the graph structures constructed for dataset1 and dataset4, respectively. There are 400 nodes and 486 edges in Figure 9, and 190 nodes and 276 edges in Figure 10. It can be seen that most of the nodes in the graph constructed by the BAKNN method have only one connected edge. This leads to an inability to obtain the structural information of the original data, resulting in unsatisfactory model accuracy. Thus, BAKNN is improved as AKNN. The weight coefficient in AKNN is set to \(\gamma=1.75\), and the graphs for dateset1 and dateset4 are presented in Figures 11 and 12, respectively. Figure 11 contains 400 nodes and 3362 edges, while Figure 12 contains 190 nodes and 1411 edges. These results are obtained because there is very little weight difference between nodes with different distances in BAKNN, and the calculation cost of adding an edge to a node is too high; therefore, BAKNN always tends to choose a smaller k value to ensure a high p value. In contrast, AKNN increases the weight difference of nodes with different distances by improving the weight function and accuracy calculation function. By introducing calculation coefficients and weight coefficients, the calculation cost of adding an edge in the method is adjusted, and the data characteristics can be extracted fully to construct a structure graph containing more global information. In this paper, the value of the weight coefficient \(\gamma\) in the AKNN method is also studied. Table 3 presents the accuracy of AKNN-DMGCN for different values of \(\gamma\), demonstrating that the choice of weight coefficient has a major impact on method performance. The performance of the method is the highest when \(\gamma\) is approximately 1.75. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Diagnostic Accuracy (\%)**} \\ \cline{2-5}  & **BAKNN-GCN** & **AKNN-GCN** & **BAKNN-DMGCN** & **AKNN-DMGCN** \\ \hline Dataset1 & 95.11\% & 96.57\% & 96.05\% & 97.22\% \\ Dataset2 & 88.69\% & 91.09\% & 91.92\% & 95.56\% \\ Dataset3 & 89.68\% & 92.71\% & 91.27\% & 94.26\% \\ Dataset4 & 88.49\% & 89.61\% & 92.41\% & 95.01\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fault diagnosis accuracy of HVCB using different methods.