## 1 Introduction
[22] proposed a model to identify organizational leaders from information such as teaching change over time and opinions of the workgroup. They found low levels of accuracy when there was not much information about the leader. In other studies, [PERSON] et al. [23] used a survey that was applied to employees and leaders of an organization to collect relevant information and validate a method for identifying central nodes. They found that not every node with too many connections meeting the centrality criteria is a leader at present. Additionally, other studies used different online social networks or digital collaboration to strengthen the ties of the nodes, meanwhile, acquiring more information to better identify leaders (see [PERSON] et al. [24; 25]). In particular, a topic that has been of great interest in network analysis is the use of machine learning techniques to identify potential leaders. For example, [PERSON] et al. [26] used statistical machine learning methods on graphs to predict emerging researchers through digital networks. They reached a 81.52% prediction accuracy. [PERSON] et al. [27] had an idea more focused on deep learning techniques, using a fuzzy graph model. This study was able to demonstrate that this type of technique can obtain 87.5% of accuracy in the identification of leaders. [PERSON] et al. [28] combined both classical and deep learning methods and used online information about the dependencies of the global supply chain to identify potential leaders. They reached an accuracy of 80.02%. To the best of our knowledge, most studies for the identification of potential leaders have focused on large-scale projects (see [PERSON] et al. [29]), and not much emphasis has been placed on understanding whether this can be replicated in different cultural context and scale projects. The reason for studying only large-scale projects is linked to the fact that the use of more information from the nodes improves the accuracy in identifying potential leaders. Therefore, in this article, we seek to validate the model proposed by [PERSON] et al. [28] to find potential leaders using information from different projects. Among these six projects, two of them were developed in different countries by a company that offers intelligent automation logistics solutions and quality software in healthcare, communication, commerce, and other areas. Two projects are from a global consulting company, and the remaining two projects are from a research team in an international manufacturing company (see [PERSON] et al. [30]). Our methodology aims to distinguish leaders from the broader staff population. Our study encompasses six projects--A, B, C, D, E, and F--providing a more robust foundation for our research findings compared to the single focus of just one project proposed by [PERSON] et al. [28]. This approach mitigates potential bias in selecting the most effective method. In addition to techniques used by [PERSON] et al. [28] that include PageRank values for interactions, word2 vec obtaining sensible features, and statistical machine learning methods, we apply state-of-the-art automated machine learning (AutoML) (see [PERSON] et al. [31]), graph convolutional networks (GCNs) (see [PERSON] et al. [32]), and explainable artificial intelligence (XAI) techniques (see [PERSON] et al. [33]), to the identification of leaders with much more rigorous evaluation on various projects in different industries and organizational contexts. AutoML methods automate the process efficiently by selecting the best algorithm and parameter configurations for the datasets provided. The H2O package we use provides a diverse range of algorithms, including gradient boosting machines (GBMs), random forests, extreme gradient boosting (XGBoost), StackedEnsemble, and generalized linear models (GLMs) (see [PERSON] et al. [34]). Graph convolutional networks (GCNs) solve the problem of learning from graph-structured data by leveraging the unique abilities of graphs to capture relations among data.