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Applied Sciences
Volume 14
Issue 20
10.3390/app14209461
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Article

Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning

by
Yunbo Xie
1,
Jose D. Meisel
2,*,
Carlos A. Meisel
2,
Juan Jose Betancourt
2,
Jianqi Yan
1 and
Roberto Bugiolacchi
3
1
School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa 999078, Macau, China
2
Facultad de Ingeniería, Universidad de Ibagué, Ibagué 730001, Colombia
3
State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Taipa 999078, Macau, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(20), 9461; https://doi.org/10.3390/app14209461
Submission received: 2 September 2024 / Revised: 10 October 2024 / Accepted: 11 October 2024 / Published: 16 October 2024
(This article belongs to the Special Issue Data Mining and Machine Learning in Social Network Analysis)
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Abstract

Over the past few decades, the study of leadership theory has expanded across various disciplines, delving into the intricacies of human behavior and defining the roles of individuals within organizations. Its primary objective is to identify leaders who play significant roles in the communication flow. In addition, behavioral theory posits that leaders can be distinguished based on their daily conduct, while social network analysis provides valuable insights into behavioral patterns. Our study investigates five and six types of social networks frequently observed in different organizations. This study is conducted using datasets we collected from an IT company and public datasets collected from a manufacturing company for the thorough evaluation of prediction performance. We leverage PageRank and effective word embedding techniques to obtain novel features. State-of-the-art performance is obtained using various statistical machine learning methods, graph convolutional networks (GCN), automated machine learning (AutoML), and explainable artificial intelligence (XAI). More specifically, our approach can achieve state-of-the-art performance with an accuracy close to 90% for leaders identification with data from projects of different types. This investigation contributes to the establishment of sustainable leadership practices by aiding organizations in retaining their leadership talent.
Keywords: social network analysis; explainable artificial intelligence; automated machine learning; PageRank; e-HRM; identification of potential leaders social network analysis; explainable artificial intelligence; automated machine learning; PageRank; e-HRM; identification of potential leaders

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MDPI and ACS Style

Xie, Y.; Meisel, J.D.; Meisel, C.A.; Betancourt, J.J.; Yan, J.; Bugiolacchi, R. Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning. Appl. Sci. 2024, 14, 9461. https://doi.org/10.3390/app14209461

AMA Style

Xie Y, Meisel JD, Meisel CA, Betancourt JJ, Yan J, Bugiolacchi R. Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning. Applied Sciences. 2024; 14(20):9461. https://doi.org/10.3390/app14209461

Chicago/Turabian Style

Xie, Yunbo, Jose D. Meisel, Carlos A. Meisel, Juan Jose Betancourt, Jianqi Yan, and Roberto Bugiolacchi. 2024. "Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning" Applied Sciences 14, no. 20: 9461. https://doi.org/10.3390/app14209461

APA Style

Xie, Y., Meisel, J. D., Meisel, C. A., Betancourt, J. J., Yan, J., & Bugiolacchi, R. (2024). Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning. Applied Sciences, 14(20), 9461. https://doi.org/10.3390/app14209461

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