Machine learning of network inference enhancement from noisy measurements

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.