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June 13, 2026Frontiers in PhysicsOpen Access

Riemannian Geometry Attention Heterogeneous Graph Network for complex networks: uncertainty modeling of signal propagation and cross-entity risk prediction in social and ESG governance networks

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Authors

JZJixian Zhang

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Overview

Randomized trial models signal propagation uncertainty in complex networks, suggesting a novel approach for ESG risk governance.

Key Points

  • The aim is to develop a model that captures uncertainty in signal propagation and risk prediction in social and ESG governance networks.
  • Proposed the Riemannian Geometry-Aware Heterogeneous Graph Network (RGA-HGN) integrating geometry and attention mechanisms.
  • Constructed a heterogeneous network using multi-source public data from 30 Dow Jones enterprises.
  • Compared model performance against 9 baseline models on three core tasks.
  • RGA-HGN significantly outperformed all baseline models on risk prediction tasks.
  • Achieved improved accuracy in modeling signal propagation uncertainty.
  • Demonstrated the effectiveness of geometric deep learning in analyzing social and ESG networks.

Cite This Study

Jixian Zhang (2026) studied this question.

synapsesocial.com/papers/6a2cf2a9faef96ed7f055651https://doi.org/10.3389/fphy.2026.1820346
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