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Title: Identifying potential managerial personnel using PageRank and social network analysis : the case study of a European IT company
Authors: Chan, JYK
Wang, Z 
Xie, Y
Meisel, CA
Meisel, JD
Solano, P
Murillo, H
Issue Date: Aug-2021
Source: Applied sciences, Aug. 2021, v. 11, no. 15, 6985
Abstract: Behavioral theory assumes that leaders can be identified by their daily behaviors. Social network analysis helps to understand behavioral patterns within their social networks. This work considers leaders as the managerial personnel of the organization and differentiates managements from non-managerial staff by their behavior with five different types of interactions with PageRank and their attributes in modern organizations. PageRank and word embedding using word2vec with phrases from features are adopted to extract new features for the identification of managerial staff. Both traditional machine learning methods and graph neural networks are utilized with real-world data from an Austrian IT company called Knapp System Integration. Our experimental results show that the proposed new features extracted using PageRank with different types of interactions and word2vec with phrases significantly improve the identification accuracy. We also propose to use graph neural networks as an effective learning algorithm to identify managers from organizations. Our approach can identify managerial staff with an accuracy of around 80%, which demonstrates that managers could be identified through social network analysis. By analyzing the behaviors of members, the proposed method is effective as a performance appraisal tool for organizations. The study facilitates sustainable management by helping organizations to retain managerial talents or to invite potential talents to join the management team.
Keywords: E-HRM
Graph convolutional network
Performance appraisal
Social network analysis
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Applied sciences 
EISSN: 2076-3417
DOI: 10.3390/app11156985
Rights: © 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (
The following publication Chan, J.Y.K.; Wang, Z.; Xie,Y.; Meisel, C.A.; Meisel, J.D.; Solano,P.; Murillo, H. Identifying PotentialManagerial Personnel UsingPageRank and Social NetworkAnalysis: The Case Study of aEuropean IT Company. Appl. Sci.2021, 11, 6985 is available at
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