## 2 Materials and Methods
### Interpretability and Feature Importance
#### 2.7.1 Model Interpretability
First, the accuracy level that was achieved in this study was obtained with only basic information from the e-mails in the project from the IT company due to confidentiality issues. In the international manufacturing company, the accuracy level was obtained with six scales of ties in interactions. The information available was the origin and destination of the e-mail, keywords, and whether or not it had information on attachments in the project in the IT company. And in the projects in the international manufacturing company, the information available was the advice-seeking interactions among workers. Therefore, future studies may evaluate the possibility of including additional information on variables to increase the robustness and potential of the model in the identification of potential leaders. Future investigations might include other classification metrics such as SimRank, Katz centrality, or Graph Bidirectional Encoder Representations from Transformers (Graph-BERT) and other evaluation methods such as recall or F1-score, which are measures that could improve the accuracy of the model. Furthermore, in our experiments, we primarily employed SHapley Additive exPlanations (SHAP) as the XAI method to identify relatively significant features, resulting in improved classification accuracy. Nevertheless, there exist alternative XAI methods that we may explore in the future, such as Local Interpretable Model-Agnostic Explanations (LIME), Anchors, Individual Conditional Expectation (ICE), Partial Dependence Plot (PDP), etc. These approaches have the potential to achieve even higher accuracy. While the model shows high accuracy in selecting leaders, the test of its effectiveness depends on its application in real-world scenarios. The leaders chosen through our model must undergo evaluation over time from a pragmatic perspective to determine if they perform at a satisfactory level. The model may be seen as valid and useful if they do perform satisfactorily. It requires a significant number of these applications to actually take place in a large number of companies and projects. During unseen situations mentioned above in real-world applications, incremental learning, also known as lifelong learning, is a machine learning technique where a model progressively acquires knowledge and improves its capabilities while retaining previously learned information. This approach is particularly useful for scenarios where data are acquired over time ([PERSON] et al. [86]). However, incremental learning is a highly challenging subject, which continues to evolve and remains ongoing. Many top scholars are making great efforts to tackle the challenges. This comes with many difficulties, such as ensuring that the model does not forget previously learned knowledge, also known as catastrophic forgetting, and managing computational complexity as the model evolves with new data ([PERSON] et al. [87]). In our current research, the complexity and workload of adapting existing models to learn from continuously evolving data, while preventing catastrophic forgetting, is considerable. Due to these difficulties, it would require substantial effort and experiments to be implemented successfully. Therefore, we plan to address this in the future, to devote adequate resources and time to properly explore incremental learning for potential leaders identification in social network analysis. ## 7 Conclusions

In this work, for the identification of potential leaders in different projects, we obtained good results with high-level performance compared to those of previous work. The proposed model, through SNA, statistical machine learning, and deep learning using convolutional neural networks (GCNs) with graphs, AutoML, and XAI helps identify potential project leaders who have performed well in the company and fulfill similar tasks in other projects or other divisions of the company. This would facilitate the task of finding talent internally within the organization.