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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2604.16074 |
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| _version_ | 1866914483316719616 |
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| author | Huber, Stefan Kaaser, Dominik |
| author_facet | Huber, Stefan Kaaser, Dominik |
| contents | We study the patient zero problem in epidemic spreading processes in the independent cascade model and propose a geometric approach for source reconstruction. Using Johnson-Lindenstrauss projections, we embed the contact network into a low-dimensional Euclidean space and estimate the infection source as the node closest to the center of gravity of infected nodes. Simulations on Erdős-Rényi graphs demonstrate that our estimator achieves meaningful reconstruction accuracy despite operating on compressed observations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16074 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Finding Patient Zero via Low-Dimensional Geometric Embeddings Huber, Stefan Kaaser, Dominik Computational Geometry Social and Information Networks We study the patient zero problem in epidemic spreading processes in the independent cascade model and propose a geometric approach for source reconstruction. Using Johnson-Lindenstrauss projections, we embed the contact network into a low-dimensional Euclidean space and estimate the infection source as the node closest to the center of gravity of infected nodes. Simulations on Erdős-Rényi graphs demonstrate that our estimator achieves meaningful reconstruction accuracy despite operating on compressed observations. |
| title | Finding Patient Zero via Low-Dimensional Geometric Embeddings |
| topic | Computational Geometry Social and Information Networks |
| url | https://arxiv.org/abs/2604.16074 |