Topology-Aware Neutrosophic Graph Structures for Modeling Innovation Performance of Agricultural Technology Enterprises in the Digital Economy Era
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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
University of New Mexico
2025-07-01
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| Series: | Neutrosophic Sets and Systems |
| Subjects: | |
| Online Access: | https://fs.unm.edu/NSS/38TopologyAware.pdf |
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| Summary: | 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. |
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| ISSN: | 2331-6055 2331-608X |