用统一隐状态建模网络性能，提升跨任务泛化能力
PLATONT: Learning a Platonic Representation for Unified Network Tomography
- 将延迟、丢包率等指标视为共享隐状态的投影
- 在真实与合成数据上均超越现有方法，鲁棒性更强
- 适合需要多维度网络感知的系统设计者
网络探查旨在通过外部观测推断隐藏的网络状态，如链路性能、流量负载和拓扑结构。现有方法通常分任务处理，依赖有限的任务特定信号，限制了泛化性和可解释性。本文提出PLATONT，一个统一框架，将不同网络指标（如延迟、丢包、带宽）建模为共享隐网络状态的投影。基于柏拉图表征假说，该框架通过多模态对齐与对比学习学习此隐状态。通过在共享隐空间中联合训练多个探查任务，构建紧凑且结构化的表征，提升跨任务泛化能力。在合成与真实数据集上的实验表明，PLATONT在链路估计、拓扑推断和流量预测任务中持续优于现有方法，精度更高，且在不同网络条件下更具鲁棒性。
原文摘要 · Abstract (English)
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.
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