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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.15251 |
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| _version_ | 1866912718613643264 |
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| author | Du, Chengze Xu, Heng Yu, Zhiwei Liu, Bo Li, Jialong |
| author_facet | Du, Chengze Xu, Heng Yu, Zhiwei Liu, Bo Li, Jialong |
| contents | Network tomography aims to infer hidden network states, such as link performance, traffic load, and topology, from external observations. Most existing methods solve these problems separately and depend on limited task-specific signals, which limits generalization and interpretability. We present PLATONT, a unified framework that models different network indicators (e.g., delay, loss, bandwidth) as projections of a shared latent network state. Guided by the Platonic Representation Hypothesis, PLATONT learns this latent state through multimodal alignment and contrastive learning. By training multiple tomography tasks within a shared latent space, it builds compact and structured representations that improve cross-task generalization. Experiments on synthetic and real-world datasets show that PLATONT consistently outperforms existing methods in link estimation, topology inference, and traffic prediction, achieving higher accuracy and stronger robustness under varying network conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15251 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PLATONT: Learning a Platonic Representation for Unified Network Tomography Du, Chengze Xu, Heng Yu, Zhiwei Liu, Bo Li, Jialong Machine Learning Networking and Internet Architecture Network tomography aims to infer hidden network states, such as link performance, traffic load, and topology, from external observations. Most existing methods solve these problems separately and depend on limited task-specific signals, which limits generalization and interpretability. We present PLATONT, a unified framework that models different network indicators (e.g., delay, loss, bandwidth) as projections of a shared latent network state. Guided by the Platonic Representation Hypothesis, PLATONT learns this latent state through multimodal alignment and contrastive learning. By training multiple tomography tasks within a shared latent space, it builds compact and structured representations that improve cross-task generalization. Experiments on synthetic and real-world datasets show that PLATONT consistently outperforms existing methods in link estimation, topology inference, and traffic prediction, achieving higher accuracy and stronger robustness under varying network conditions. |
| title | PLATONT: Learning a Platonic Representation for Unified Network Tomography |
| topic | Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2511.15251 |