A simple and flexible algorithm to generate real-world networks
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910845242441728 |
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| author | Morais, João Pedro C. Interian, Ruben |
| author_facet | Morais, João Pedro C. Interian, Ruben |
| contents | This study introduces an algorithm that generates undirected graphs with three main characteristics of real-world networks: scale-freeness, short distances between nodes (small-world phenomenon), and large clustering coefficients. The main idea is to perform random walks across the network and, at each iteration, add special edges with a decreasing probability to link more distant nodes, following a specific probability distribution. A key advantage of our algorithm is its simplicity and flexibility in creating networks with different characteristics without using global information about network topology. We show how the parameters can be adjusted to generate networks with specific average distances and clustering coefficients, maintaining a long-tailed degree distribution. The implementation of our algorithm is publicly available on a GitHub repository. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18579 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A simple and flexible algorithm to generate real-world networks Morais, João Pedro C. Interian, Ruben Social and Information Networks Physics and Society This study introduces an algorithm that generates undirected graphs with three main characteristics of real-world networks: scale-freeness, short distances between nodes (small-world phenomenon), and large clustering coefficients. The main idea is to perform random walks across the network and, at each iteration, add special edges with a decreasing probability to link more distant nodes, following a specific probability distribution. A key advantage of our algorithm is its simplicity and flexibility in creating networks with different characteristics without using global information about network topology. We show how the parameters can be adjusted to generate networks with specific average distances and clustering coefficients, maintaining a long-tailed degree distribution. The implementation of our algorithm is publicly available on a GitHub repository. |
| title | A simple and flexible algorithm to generate real-world networks |
| topic | Social and Information Networks Physics and Society |
| url | https://arxiv.org/abs/2502.18579 |