Graph Attention Networks for Organizational Trust Influence Mapping: Identifying Informal Leaders, Bridge Connectors, and Trust Propagation Patterns Using the DEEP Framework

Organizational trust does not follow formal hierarchies only. Informal networks via mentorship, collaboration, and peer influence are often the difference between success or failure for a transformation. In this paper, we employ Graph Attention Networks (GATs) to model trust influence patterns in organizations, with the node features being 53-competency trust profiles from the DEEP (Drive, Engage, Empower, Purpose-align) Framework. Trust graphs are constructed from N=12,400 employees in 14 organizations with |E|=47,312 weighted directed edges, and four sources of interaction: formal reporting, 360-degree nominations, project co-participation, and meeting co-attendance. The 2-layer GAT with 4 attention heads achieved a R^2=0.879 on Trust Influence Score (TIS) prediction, which is 46.9% higher than PageRank, 11.1% higher than GCN and 23.5% higher than Node2Vec. The pipeline is formalized by four algorithms: Multi-Source Graph Construction, GAT Trust Influence Prediction, Bridge Leader Identification and Trust Propagation Simulation. Key findings: (1) 23.4% of Exceptional trust leaders lack management titles, suggesting an informal trust infrastructure invisible to org charts; (2) high-trust organizations have 2.3x higher network density and 1.7x higher clustering; (3) ablation analysis shows bridge leaders (4.7% of population) account for 31.2% of organizational trust variance; (4) the Engage dimension is the most effective predictor of cross-cluster influence (β=0.41, p<.001); (5) GAT attention weights show asymmetric trust influence patterns with upward trust flow (subordinate→manager) being 1.4x stronger than downward flow. This work provides a methodology to identify and leverage informal trust infrastructure during purpose-driven organizational transformation.

Graph Neural Networks, Graph Attention Networks, Trust Influence, Organizational Network Analysis, Informal Leadership

1. R. Cross and A. Parker, The Hidden Power of Social Networks. HBS Press, 2004. [Google Scholar] [Crossref]

2. R. Cross and R. J. Thomas, “How top talent uses networks and where rising stars get trapped,” Org. Dynamics, vol. 37, pp. 165–180, 2008. [Google Scholar] [Crossref]

3. R. Cross, A. Parker, and L. Borgatti, “Knowing what we know: Supporting knowledge creation and sharing in social networks,” Org. Dynamics, vol. 31, pp. 100–120, 2001. [Google Scholar] [Crossref]

4. M. Granovetter, “The strength of weak ties,” Am. J. Sociology, vol. 78, pp. 1360–1380, 1973. [Google Scholar] [Crossref]

5. R. S. Burt, Structural Holes: The Social Structure of Competition. Harvard Univ. Press, 1992. [Google Scholar] [Crossref]

6. D. W. Bracken, A. H. Church, and J. W. Fleenor, The Handbook of Strategic 360 Feedback. Oxford Univ. Press, 2020. [Google Scholar] [Crossref]

7. McKinsey & Company, “State of AI Trust in 2026: Shifting to the Agentic Era,” Mar. 2026. [Google Scholar] [Crossref]

8. Z. Wu, S. Pan, F. Chen et al., “Comprehensive survey on graph neural networks,” IEEE Trans. Neural Networks, vol. 32, pp. 4–24, 2021. [Google Scholar] [Crossref]

9. T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proc. ICLR, 2017. [Google Scholar] [Crossref]

10. L. Page, S. Brin, R. Motwani, and T. Winograd, “The PageRank citation ranking: Bringing order to the web,” Stanford InfoLab, 1999. [Google Scholar] [Crossref]

11. P. Velickovic, G. Cucurull, A. Casanova et al., “Graph attention networks,” in Proc. ICLR, 2018. [Google Scholar] [Crossref]

12. W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proc. NeurIPS, 2017. [Google Scholar] [Crossref]

13. K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?,” in Proc. ICLR, 2019. [Google Scholar] [Crossref]

14. Z. Hu, Y. Dong, K. Wang, and Y. Sun, “Heterogeneous graph transformer,” in Proc. WWW, 2020. [Google Scholar] [Crossref]

15. Pomnar, “DEEP Framework: Drive, Engage, Empower, Purpose-align Leadership Assessment,” Working Paper, 2025. [Google Scholar] [Crossref]

16. S. M. R. Covey, Trust & Inspire. New York: Simon & Schuster, 2022. [Google Scholar] [Crossref]

17. World Economic Forum, “Why rebuilding trust is key for the Intelligent Age of AI,” WEF Stories, Jan. 2025. [Google Scholar] [Crossref]

18. PwC, “2026 AI Business Predictions: From Mature Systems to Emerging Tools,” 2026. [Google Scholar] [Crossref]

19. D. J. Brass, “Being in the right place: A structural analysis of individual influence in an organization,” Admin. Sci. Quarterly, vol. 29, pp. 518–539, 1984. [Google Scholar] [Crossref]

20. S. P. Borgatti and P. C. Foster, “The network paradigm in organizational research: A review and typology,” J. Management, vol. 29, pp. 991–1013, 2003. [Google Scholar] [Crossref]

21. P. Balkundi and M. Kilduff, “The ties that lead: A social network approach to leadership,” The Leadership Quarterly, vol. 17, pp. 419–439, 2006. [Google Scholar] [Crossref]

22. D. Obstfeld, “Social networks, the tertius iungens orientation, and involvement in innovation,” Admin. Sci. Quarterly, vol. 50, pp. 100–130, 2005. [Google Scholar] [Crossref]

23. R. C. Mayer, J. H. Davis, and F. D. Schoorman, “An integrative model of organizational trust,” AMR, vol. 20, pp. 709–734, 1995. [Google Scholar] [Crossref]

24. D. J. McAllister, “Affect- and cognition-based trust as foundations for interpersonal cooperation,” AMJ, vol. 38, pp. 24–59, 1995. [Google Scholar] [Crossref]

25. C. Edmondson, The Fearless Organization. Hoboken, NJ: Wiley, 2018. [Google Scholar] [Crossref]

26. P. Lencioni, The Five Dysfunctions of a Team. San Francisco: Jossey-Bass, 2002. [Google Scholar] [Crossref]

27. Grover and J. Leskovec, “Node2Vec: Scalable feature learning for networks,” in Proc. KDD, 2016. [Google Scholar] [Crossref]

28. J. Avolio and W. L. Gardner, “Authentic leadership development,” The Leadership Quarterly, vol. 16, pp. 315–338, 2005. [Google Scholar] [Crossref]

29. F. O. Walumbwa et al., “Authentic leadership: Development and validation,” J. Management, vol. 34, pp. 89–126, 2008. [Google Scholar] [Crossref]

30. V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,” J. Stat. Mech., P10008, 2008. [Google Scholar] [Crossref]

31. S. M. Lundberg and S. I. Lee, “Unified approach to interpreting model predictions,” NeurIPS, 2017. [Google Scholar] [Crossref]

32. Pareja et al., “EvolveGCN: Evolving graph convolutional networks for dynamic graphs,” in Proc. AAAI, 2020. [Google Scholar] [Crossref]

33. European Parliament, “GDPR,” Regulation (EU) 2016/679, 2016. [Google Scholar] [Crossref]

34. B. S. Zadorozhny et al., “Predicting leadership through trait EI,” Behav. Sci., vol. 15, 2025. [Google Scholar] [Crossref]

35. S. Talebi et al., “ML for employee turnover: Systematic review,” Eng. Reports, vol. 7, 2025. [Google Scholar] [Crossref]

36. D. H. Schmidt et al., “The data-driven leader,” J. Mgmt Dev., vol. 42, 2023. [Google Scholar] [Crossref]

37. Training Magazine, “Trust & Inspire: The Leadership Shift,” Feb. 2026. [Google Scholar] [Crossref]

38. B. M. Bass and R. E. Riggio, Transformational Leadership, 2nd ed. Lawrence Erlbaum, 2006. [Google Scholar] [Crossref]

39. T. Chen and C. Guestrin, “XGBoost,” in Proc. KDD, 2016. [Google Scholar] [Crossref]

40. Mishra et al., “Predicting OCB using AI,” Acta Psychologica, vol. 252, 2025. [Google Scholar] [Crossref]

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