Model-agnostic network inference enhancement from noisy measurements via
  curriculum learning
Kai Wu Yuanyuan Li Jing Liu
Noise is a pervasive element within real-world measurement data,
significantly undermining the performance of network inference models. However,
the quest for a comprehensive enhancement framework capable of bolstering noise
resistance across a diverse array of network inference models has remained
elusive. Here, we present an elegant and efficient framework tailored to
amplify the capabilities of network inference models in the presence of noise.
Leveraging curriculum learning, we mitigate the deleterious impact of noisy
samples on network inference models. Our proposed framework is model-agnostic,
seamlessly integrable into a plethora of model-based and model-free network
inference methods. Notably, we utilize one model-based and three model-free
network inference methods as the foundation. Extensive experimentation across
various synthetic and real-world networks, encapsulating diverse nonlinear
dynamic processes, showcases substantial performance augmentation under varied
noise types, particularly thriving in scenarios enriched with clean samples.
This framework's adeptness in fortifying both model-free and model-based
network inference methodologies paves the avenue towards a comprehensive and
unified enhancement framework, encompassing the entire spectrum of network
inference models. Available Code: https://github.com/xiaoyuans/MANIE.