## 2 Materials and Methods
### Interpretability and Feature Importance
#### 2.7.1 Model Interpretability
(2020), incorporating information from the project mail flow to each node and using deep learning techniques, obtained an average of 79.64% of accuracy in the identification of potential leaders. On the other hand, [PERSON] et al. (2021) used fuzzy learning incorporating only survey information to achieve an average percentage of 85.4% in the accuracy of identifying potential leaders. Different authors have used classical prediction models such as SVM and fuzzy graph model to identify potential leaders in large-scale projects based on information acquired from surveys of leaders. These models have generated good results, with average accuracy levels between 74.68% and 83.39% ([PERSON] et al. (2020); [PERSON] et al. (2020); [PERSON] et al. (2020)). In this study, we employed a model that includes information extracted from the relationships of the PTR in projects and incorporated into a convolutional neural network with graphs to identify potential leaders using PageRank. We used XAI to select the important features, and the results show an average accuracy level of higher than 86%. These results indicate that the model can be a good methodology for identifying potential leaders with information on different projects. Additionally, the results show that the proposed model obtains accuracy levels above 87% for the real-world project A from the IT company. In project E and project F from the manufacturing company, the model obtains an accuracy level above 85% and 91%, respectively. According to our knowledge, the studies that have analyzed this problem show accuracy between 61.36% and 87.5% in projects proposed by [PERSON] et al. (2020); [PERSON] et al. (2020); [PERSON] et al. (2020); however, they did not evaluate the level of accuracy in the identification of potential \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Projects** & **Features** & **AutoML** & **SVM-G** & **SVM-L** & **SVM-S** & **K-NN** & **MLP** & **GCN** \\ \hline \multirow{4}{*}{Project D} & F & 0.7991 & 0.7130 & 0.7086 & 0.7556 & 0.7151 & 0.7940 & 0.8437 \\  & F+P & 0.7818 & 0.7554 & 0.7131 & 0.7436 & 0.7350 & 0.8295 & 0.8504 \\  & RFE(F+P) & 0.8108 & 0.7591 & 0.7216 & 0.7622 & 0.7519 & 0.8264 & 0.8587 \\  & XAI(F+P) & 0.8383 & 0.7759 & 0.7398 & 0.7816 & 0.7823 & 0.8298 & 0.8604 \\ \hline \multirow{4}{*}{Project E} & F & 0.8157 & 0.7804 & 0.7020 & 0.7150 & 0.7350 & 0.7669 & 0.8335 \\  & F+P & 0.8062 & 0.7933 & 0.7040 & 0.7241 & 0.7608 & 0.7725 & 0.8453 \\  & RFE(F+P) & 0.8186 & 0.7992 & 0.7273 & 0.7607 & 0.7790 & 0.7819 & 0.8512 \\  & XAI(F+P) & 0.8234 & 0.8104 & 0.7519 & 0.7815 & 0.7796 & 0.7994 & 0.8541 \\ \hline \multirow{4}{*}{Project F} & F & 0.7936 & 0.7768 & 0.7679 & 0.7650 & 0.7857 & 0.7626 & 0.8310 \\  & F+P & 0.8009 & 0.7933 & 0.7640 & 0.7740 & 0.7808 & 0.7725 & 0.8440 \\ \cline{1-1}  & RFE(F+P) & 0.8458 & 0.8152 & 0.7916 & 0.7813 & 0.8023 & 0.7834 & 0.8571 \\ \cline{1-1}  & XAI(F+P) & 0.9141 & 0.8476 & 0.8002 & 0.8106 & 0.8279 & 0.8071 & 0.8630 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: _Cont._leaders in other different projects. These results show the indication that the proposed model is robust and capable of identifying potential leaders in projects of different sizes while maintaining good levels of accuracy. Furthermore, by employing the XAI method to select the most crucial features, statistical machine learning methods, AutoML methods, and GCN demonstrate significantly improved accuracy, as illustrated in Table 14. GCN accuracy can be elevated by as much as 4.3%, statistical machine learning methods can see an increase of up to 7.1% in accuracy, and AutoML methods can experience an impressive increase of up to 12.1% in accuracy. In summary, GCN and AutoML methods consistently achieve higher accuracy than statistical machine learning methods. ## 6 Limitations and Future Work

Despite the degree of accuracy and functionalities of the model, it is important to indicate some limitations of the study.