## 2 Materials and Methods
### Interpretability and Feature Importance
#### 2.7.1 Model Interpretability
In project E, the accuracy of AutoML is a little bit lower than GCN, but still higher than all statistical machine learning techniques. The features selected by XAI lead to higher accuracy than those selected by RFE. In project F, the AutoML with XAI has an accuracy of more than 91%, higher than that of GCN by around 5%. In summary, our experiments indicate that AutoML has the potential to achieve higher accuracy than statistical machine learning methods, because of its ability to automatically select the most suitable model. As can be seen from Table 14, AutoML proves to be adaptable and effective for our projects. Including XAI aids in selecting significant features for the training stage, improving accuracy across all methods, including AutoML, statistical machine learning, and GCN models. \begin{table}
\begin{tabular}{c c c} \hline
**Projects** & **AutoML** & **GCN** \\ \hline A & 64.10 & 117.16 \\ B & 60.09 & 97.51 \\ C & 64.17 & 111.38 \\ D & 61.37 & 103.28 \\ E & 61.40 & 86.48 \\ F & 63.09 & 99.34 \\ \hline \end{tabular}
\end{table} 
Table 13: Time (s) by automated machine learning and GCN on features selected by XAI method from (F+P) feature sets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Projects** & **Features** & **AutoML** & **SVM-G** & **SVM-L** & **SVM-S** & **K-NN** & **MLP** & **GCN** \\ \hline \multirow{4}{*}{Project A} & F & 0.8186 & 0.7152 & 0.7089 & 0.7042 & 0.7196 & 0.7106 & 0.8326 \\  & F+P & 0.8158 & 0.7171 & 0.7098 & 0.7093 & 0.7225 & 0.7129 & 0.8406 \\  & RFE(F+P) & 0.8357 & 0.7338 & 0.7092 & 0.7140 & 0.7382 & 0.7147 & 0.8652 \\  & XAI(F+P) & 0.8416 & 0.7432 & 0.7183 & 0.7265 & 0.7480 & 0.7228 & 0.8761 \\ \hline \multirow{4}{*}{Project B} & F & 0.8082 & 0.7761 & 0.7758 & 0.7869 & 0.7851 & 0.7769 & 0.8823 \\  & F+P & 0.8152 & 0.7810 & 0.7764 & 0.7866 & 0.7819 & 0.7774 & 0.8861 \\  & RFE(F+P) & 0.8450 & 0.7857 & 0.7865 & 0.7924 & 0.8237 & 0.7796 & 0.8968 \\  & XAI(F+P) & 0.8894 & 0.7907 & 0.7908 & 0.7948 & 0.8330 & 0.7812 & 0.9071 \\ \hline \multirow{4}{*}{Project C} & F & 0.7850 & 0.7360 & 0.7041 & 0.7313 & 0.7162 & 0.8227 & 0.8035 \\  & F+P & 0.7948 & 0.7555 & 0.7135 & 0.7436 & 0.7350 & 0.8295 & 0.8214 \\ \cline{1-1}  & RFE(F+P) & 0.8597 & 0.7643 & 0.7381 & 0.7663 & 0.7391 & 0.8304 & 0.8303 \\ \cline{1-1}  & XAI(F+P) & 0.8818 & 0.7796 & 0.7544 & 0.7930 & 0.7537 & 0.8396 & 0.8356 \\ \hline \end{tabular}
\end{table} 
Table 14: Accuracy by using automated machine learning, statistical machine learning techniques, and GCN for different sets of features. ## 5 Discussion

The identification of leaders from the network analysis in different projects has been a topic of great interest because they tend to be the nodes with the highest centrality degree. These people with higher centrality are at high levels of the company because they have a high flow of information, management of internal relationships, and good adaptation to situations of high uncertainty ([PERSON] et al. (2018); [PERSON] et al. (2019); [PERSON] et al. (2020)). In this study, we found that when using explainable AI, AutoML, and graph convolutional networks ([PERSON] et al. (2020)), high accuracy can be obtained in the identification of leaders of various projects in different industries. The results demonstrate the efficiency of the methodology used in this study. It is possible to see the increase in the accuracy of the classifiers as more attributes from each node are included (Table 14). The model including statistical machine learning, graph convolutional networks, AutoML methods, and feature importance method XAI generates the highest accuracy level in all projects, between 82.34% and 91.41%. These results show a better level of accuracy compared to other studies which showed an average accuracy level between 74.68% and 83.39%. For example, [PERSON] et al. (2016) achieved, on average, 82.66% of accuracy in identifying potential research leaders using deep learning with data available in the database CiteSeerx. Similarly, [PERSON] et al.