  Inferring networks from observed time series data presents a clear glimpse
into the interconnections among nodes. Network inference models, when dealing
with real-world open cases, especially in the presence of observational noise,
experience a sharp decline in performance, significantly undermining their
practical applicability. We find that in real-world scenarios, noisy samples
cause parameter updates in network inference models to deviate from the correct
direction, leading to a degradation in performance. Here, we present an elegant
and efficient model-agnostic framework tailored to amplify the capabilities of
model-based and model-free network inference models for real-world cases.
Extensive experiments across nonlinear dynamics, evolutionary games, and
epidemic spreading, showcases substantial performance augmentation under varied
noise types, particularly thriving in scenarios enriched with clean samples.
