Temporal connection probabilities in real networks
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917437334618112 |
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| author | Papadopoulos, Fragkiskos |
| author_facet | Papadopoulos, Fragkiskos |
| contents | Principled prediction of when and where links form in complex networks is a fundamental problem. We derive a closed-form non-Markovian expression for next-step connection probabilities that unifies latent hyperbolic geometry with long-range memory of past interactions. This expression yields interpretable forecasts governed by a small set of parameters. Applied to large-scale real networks, we find quantitative agreement with empirical connection probabilities and reveal how geometry and memory jointly shape link dynamics. These results establish a minimal and extensible foundation for principled probabilistic forecasting of temporal network topology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23714 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Temporal connection probabilities in real networks Papadopoulos, Fragkiskos Physics and Society Social and Information Networks Principled prediction of when and where links form in complex networks is a fundamental problem. We derive a closed-form non-Markovian expression for next-step connection probabilities that unifies latent hyperbolic geometry with long-range memory of past interactions. This expression yields interpretable forecasts governed by a small set of parameters. Applied to large-scale real networks, we find quantitative agreement with empirical connection probabilities and reveal how geometry and memory jointly shape link dynamics. These results establish a minimal and extensible foundation for principled probabilistic forecasting of temporal network topology. |
| title | Temporal connection probabilities in real networks |
| topic | Physics and Society Social and Information Networks |
| url | https://arxiv.org/abs/2604.23714 |