Topology-Aware Neutrosophic Graph Structures for Modeling Innovation Performance of Agricultural Technology Enterprises in the Digital Economy Era

Abstract

With the proliferation of smart agricultural initiatives in the digital economy, a significant 
increase in uncertainties has arisen in how stakeholders interact across different dimensions. 
Existing neutrosophic models, including neutrosophic graphs, failed to model structural realities 
that govern these interactions. To address this challenge, this study presents a novel mathematical 
framework, topology-aware neutrosophic graphs, that integrates neutrosophic graph theories with 
domain-specific topological constraints for modeling enterprise connectivity. Our key 
contributions include: 1) providing formal definition of topology-aware neutrosophic graphs 
theory that integrate neutrosophic uncertainty with structural constraints; 2) presenting 
customized operations and analytical tools for exploring graph properties under topological 
constraints; and 3) demonstrating through a detailed case study how our framework reduces 
uncertainty and improves interpretability compared to custom approaches. We study the 
applicability of the proposed framework on a real case study, and the results show the ability to 
capture uncertainty more realistically while filtering implausible relationships. Based on 
comparative analysis against topology-agnostic models, we prove the benefits of our framework 
in reducing network ambiguity and enhancing interpretability. 

DOI: 10.5281/zenodo.15843340