On Yukawa Potential Centrality for Identification of Influential Spreaders in Complex Networks
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909921445937152 |
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| author | Bazyarrezaei, Pouria Azgomi, Mohammad Abdollahi |
| author_facet | Bazyarrezaei, Pouria Azgomi, Mohammad Abdollahi |
| contents | Identifying influential nodes in complex networks is a fundamental challenge for understanding how information, influence, and contagion propagate through interconnected systems. Conventional centrality measures, particularly gravity-based models, often depend on pairwise interaction forces and a fixed radius of influence, which oversimplify the heterogeneous and dynamic nature of real networks. To overcome these limitations, this study proposes a novel non-interactive, action-based model, termed Yukawa Potential Centrality (YPC), which adapts the physical Yukawa potential to the topology of complex networks. Unlike gravity models, YPC computes a scalar potential for each node rather than pairwise forces, dynamically adjusting its radius of influence according to local structural properties. This formulation establishes a physically interpretable bridge between potential theory and network science, while significantly reducing computational complexity, from quadratic to near-linear time. The model is evaluated across both synthetic and real-world social networks, and its node rankings are compared with classical centrality indices and epidemic spreading models (SI and SIS). Experimental findings reveal that YPC exhibits a strong positive correlation with the SIS model and effectively isolates key spreaders, even within highly irregular topologies. These results demonstrate that YPC provides a scalable, adaptive, and theoretically grounded framework for influence analysis in social, biological, and communication networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19300 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On Yukawa Potential Centrality for Identification of Influential Spreaders in Complex Networks Bazyarrezaei, Pouria Azgomi, Mohammad Abdollahi Social and Information Networks Identifying influential nodes in complex networks is a fundamental challenge for understanding how information, influence, and contagion propagate through interconnected systems. Conventional centrality measures, particularly gravity-based models, often depend on pairwise interaction forces and a fixed radius of influence, which oversimplify the heterogeneous and dynamic nature of real networks. To overcome these limitations, this study proposes a novel non-interactive, action-based model, termed Yukawa Potential Centrality (YPC), which adapts the physical Yukawa potential to the topology of complex networks. Unlike gravity models, YPC computes a scalar potential for each node rather than pairwise forces, dynamically adjusting its radius of influence according to local structural properties. This formulation establishes a physically interpretable bridge between potential theory and network science, while significantly reducing computational complexity, from quadratic to near-linear time. The model is evaluated across both synthetic and real-world social networks, and its node rankings are compared with classical centrality indices and epidemic spreading models (SI and SIS). Experimental findings reveal that YPC exhibits a strong positive correlation with the SIS model and effectively isolates key spreaders, even within highly irregular topologies. These results demonstrate that YPC provides a scalable, adaptive, and theoretically grounded framework for influence analysis in social, biological, and communication networks. |
| title | On Yukawa Potential Centrality for Identification of Influential Spreaders in Complex Networks |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2511.19300 |