Metric spaces of walks and Lipschitz duality on graphs
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911125152464896 |
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| author | Arnau, R. Cortés, A. González Pérez, E. A. Sánchez Sanjuan, S. |
| author_facet | Arnau, R. Cortés, A. González Pérez, E. A. Sánchez Sanjuan, S. |
| contents | We study the metric structure of walks on graphs, understood as Lipschitz sequences. To this end, a weighted metric is introduced to handle sequences, enabling the definition of distances between walks based on stepwise vertex distances and weighted norms. We analyze the main properties of these metric spaces, which provides the foundation for the analysis of weaker forms of instruments to measure relative distances between walks: proximities. We provide some representation formulas for such proximities under different assumptions and provide explicit constructions for these cases. The resulting metric framework allows the use of classical tools from metric modeling, such as the extension of Lipschitz functions from subspaces of walks, which permits extending proximity functions while preserving fundamental properties via the mentioned representations. Potential applications include the estimation of proximities and the development of reinforcement learning strategies based on exploratory walks, offering a robust approach to Lipschitz regression on network structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Metric spaces of walks and Lipschitz duality on graphs Arnau, R. Cortés, A. González Pérez, E. A. Sánchez Sanjuan, S. Machine Learning Functional Analysis 26A16 We study the metric structure of walks on graphs, understood as Lipschitz sequences. To this end, a weighted metric is introduced to handle sequences, enabling the definition of distances between walks based on stepwise vertex distances and weighted norms. We analyze the main properties of these metric spaces, which provides the foundation for the analysis of weaker forms of instruments to measure relative distances between walks: proximities. We provide some representation formulas for such proximities under different assumptions and provide explicit constructions for these cases. The resulting metric framework allows the use of classical tools from metric modeling, such as the extension of Lipschitz functions from subspaces of walks, which permits extending proximity functions while preserving fundamental properties via the mentioned representations. Potential applications include the estimation of proximities and the development of reinforcement learning strategies based on exploratory walks, offering a robust approach to Lipschitz regression on network structures. |
| title | Metric spaces of walks and Lipschitz duality on graphs |
| topic | Machine Learning Functional Analysis 26A16 |
| url | https://arxiv.org/abs/2508.19709 |