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Network based analysis of student self governance networks and predictive role in civic participation outcomes
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  • Published: 27 January 2026

Network based analysis of student self governance networks and predictive role in civic participation outcomes

  • Jing Liu1,
  • Putu Kerti Nitiasih1,
  • Made Hery Santosa1 &
  • …
  • Putu Nanci Riastini1 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Environmental sciences
  • Environmental social sciences

Abstract

This study examines the relationship between student Self-Governance Networks (SGN) and Civic Participation Outcomes (CPO) using a network-based analytical framework. Data were collected from 237 student governance participants at a regional university in eastern China and analyzed using Social Network Analysis (SNA), Multivariate Regression (MR), and Machine Learning (ML) classification. The SGN displayed a modular network comprising 6 functional communities, including central administration, academic councils, disciplinary bodies, and interest-based organizations. Structural analysis revealed a hierarchical hub-and-spoke configuration, with the central student government serving as the primary bridge across communities, while peripheral groups remained comparatively isolated. Centrality measures varied systematically across roles, with formal leadership positions occupying structurally advantaged positions. Regression analyses controlling for demographics confirmed that network position significantly predicted civic engagement, with eigenvector centrality and the Community E–I Index emerging as consistent predictors across results. ML models, particularly XGBoost, achieved strong predictive accuracy (accuracy = 0.781, AUC-ROC = 0.842), indicating that network features reliably differentiate engaged from non-engaged students. These findings indicate that the civic benefits of governance participation are not uniformly distributed but depend on an individual’s structural position, with ties to central actors and cross-community linkages enhancing involvement. The study outlines theoretical contributions and practical implications for governance design and civic education.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Author information

Authors and Affiliations

  1. College of Education, Universitas Pendidikan Ganesha, Jalan Udayana, Bali Province, 81116, Indonesia

    Jing Liu, Putu Kerti Nitiasih, Made Hery Santosa & Putu Nanci Riastini

Authors
  1. Jing Liu
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  2. Putu Kerti Nitiasih
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  3. Made Hery Santosa
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  4. Putu Nanci Riastini
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Contributions

Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data CurationWriting - Original Draft, Writing - Review & Editing, Visualization : Jing Liu, Putu Kerti Nitiasih, Made Hery Santosa, Putu Nanci Riastini.

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Correspondence to Jing Liu.

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Cite this article

Liu, J., Nitiasih, P.K., Santosa, M.H. et al. Network based analysis of student self governance networks and predictive role in civic participation outcomes. Sci Rep (2026). https://doi.org/10.1038/s41598-025-34205-x

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  • Received: 20 May 2025

  • Accepted: 26 December 2025

  • Published: 27 January 2026

  • DOI: https://doi.org/10.1038/s41598-025-34205-x

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Keywords

  • Higher education
  • Social network analysis
  • Self-Governance network
  • Civic participation outcomes
  • Machine learning
  • Multivariate regression
  • XGBoost
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