Machine learning of network inference enhancement from noisy measurements

arXiv:2309.02050v2 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv cases clear cs.lg cs.si data experience inference machine machine learning network networks nodes noise performance practical series time series type world