Model-based reconstruction of real-world fractal 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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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911164139569152 |
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| author | Makulski, Kordian Samsel, Mateusz Lepek, Michal Fronczak, Agata Fronczak, Piotr |
| author_facet | Makulski, Kordian Samsel, Mateusz Lepek, Michal Fronczak, Agata Fronczak, Piotr |
| contents | This paper presents a versatile model for generating fractal complex networks that closely mirror the properties of real-world systems. By combining features of reverse renormalization and evolving network models, the proposed approach introduces several tunable parameters, offering exceptional flexibility in capturing the diverse topologies and scaling behaviors found in both natural and man-made networks. The model effectively replicates their key characteristics such as fractal dimensions, power-law degree distributions, and scale-invariant properties of hierarchically nested boxes. Unlike traditional deterministic models, it incorporates stochasticity into the network growth process, overcoming limitations like discontinuities in degree distributions and rigid size constraints. The model's applicability is demonstrated through its ability to reproduce the structural features of real-world fractal networks, including the Internet, the World Wide Web, and co-authorship networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04186 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model-based reconstruction of real-world fractal complex networks Makulski, Kordian Samsel, Mateusz Lepek, Michal Fronczak, Agata Fronczak, Piotr Physics and Society Disordered Systems and Neural Networks This paper presents a versatile model for generating fractal complex networks that closely mirror the properties of real-world systems. By combining features of reverse renormalization and evolving network models, the proposed approach introduces several tunable parameters, offering exceptional flexibility in capturing the diverse topologies and scaling behaviors found in both natural and man-made networks. The model effectively replicates their key characteristics such as fractal dimensions, power-law degree distributions, and scale-invariant properties of hierarchically nested boxes. Unlike traditional deterministic models, it incorporates stochasticity into the network growth process, overcoming limitations like discontinuities in degree distributions and rigid size constraints. The model's applicability is demonstrated through its ability to reproduce the structural features of real-world fractal networks, including the Internet, the World Wide Web, and co-authorship networks. |
| title | Model-based reconstruction of real-world fractal complex networks |
| topic | Physics and Society Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2502.04186 |