## 2 Materials and Methods
### Interpretability and Feature Importance
#### 2.7.1 Model Interpretability
Table 12 below takes project A as an example, showing the accuracy of our models with 10-fold cross-validation and L2 regularization. Table 13 describes the computational time by AutoML and GCN on features selected by XAI method from (F+P) feature sets in all projects. We used the H2O framework to implement AutoML. It is an open-source, distributed machine learning framework developed to scale to large datasets. It is a highly scalable, fully automated algorithm that automates the process for training numerous candidate models as well as stacked ensembles ([PERSON] et al. [82]). GCN is good at learning from graph-structured data and scales efficiently to large graphs, making them highly effective in a large number of applications and research fields, including social networks, recommendation systems, clustering, and more,as proposed by [PERSON] et al. [83]. Both AutoML and GCN models require relatively high computational resources in large-scale networks and datasets. It is, indeed, a common scenario in various applications and research fields. Nevertheless, the runtime and resource demands remain within acceptable limits, and the models are capable of running effectively ([PERSON] et al. [64; 67; 84; 85]). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Projects** & **10-Fold Cross-Validation** & **AutoML** & **SVM-G** & **SVM-L** & **SVM-S** & **K-NN** & **MLP** & **GCN** \\ \hline \multirow{8}{*}{Project A} & fold01 & 0.8424 & 0.7523 & 0.6986 & 0.7026 & 0.7439 & 0.7582 & 0.8723 \\  & fold02 & 0.8437 & 0.7341 & 0.7067 & 0.7107 & 0.7263 & 0.7137 & 0.8815 \\  & fold03 & 0.8327 & 0.7340 & 0.681 & 0.682 & 0.752 & 0.6624 & 0.8754 \\  & fold04 & 0.8559 & 0.7523 & 0.6986 & 0.7022 & 0.7426 & 0.7319 & 0.8823 \\  & fold05 & 0.8491 & 0.7522 & 0.7249 & 0.7289 & 0.7965 & 0.7319 & 0.8684 \\  & fold06 & 0.8386 & 0.7531 & 0.7250 & 0.7289 & 0.7702 & 0.7315 & 0.8892 \\  & fold07 & 0.8538 & 0.7597 & 0.7579 & 0.7619 & 0.7264 & 0.7393 & 0.8637 \\  & fold08 & 0.8295 & 0.7084 & 0.7323 & 0.7363 & 0.8032 & 0.7127 & 0.8851 \\  & fold09 & 0.8490 & 0.7519 & 0.7775 & 0.7815 & 0.6912 & 0.7219 & 0.8709 \\  & fold10 & 0.8215 & 0.7331 & 0.681 & 0.7256 & 0.7263 & 0.7137 & 0.8892 \\  & Average & 0.8416 & 0.7432 & 0.7183 & 0.7265 & 0.7480 & 0.7228 & 0.8761 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The accuracy in 10-fold cross-validation and L2 regularization of Automated Machine Learning (AutoML), statistical machine learning techniques, and Graph Convolutional Networks (GCNs) with features selected by XAI from (F+P) feature sets in project A. GCN is applied to the train–validation–test dataset partition and repeated for 10 experiments with different randomization. Table 14 shows the summary, comparing all the methods we employed, including statistical machine learning methods, graph convolutional networks (GCN), and automated machine learning (AutoML). We trained our methods on four types of feature combinations, i.e., features (F), features + PageRank values (F+P), important features selected by RFE (RFE(F+P)), and important features selected by XAI (XAI(F+P)). All experiments were repeated ten times and the average accuracy was calculated. Generally, AutoML exhibits higher accuracy than statistical machine learning methods, and approaches the performance of GCN. Regarding feature selection, both recursive feature elimination (RFE) and explainable artificial intelligence (XAI) methods lead to significantly improved classification accuracy when compared to using all original features. This suggests that features selected by RFE or XAI have a higher importance in the classification process. When comparing these two methods, XAI excels in selecting more important features than RFE. To provide specific examples, in project A, the highest accuracy of 87.61% is achieved by GCN when utilizing XAI. AutoML combined with XAI achieves an accuracy of 84.16%, surpassing statistical methods by around 10%. In project B, AutoML achieves 88.94%, which is close to the accuracy by GCN.