Related papers: Machine learning of network inference enhancement from noisy measurements

Machine learning of network inference enhancement from noisy measurements

URL: http://arxiv.org/abs/2309.02050v2
Date: Sun, 5 May 2024 14:42:07 GMT
Title: Machine learning of network inference enhancement from noisy measurements
Authors: Kai Wu, Yuanyuan Li, Jing Liu,
Abstract summary: 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, experience a sharp decline in performance. We present an elegant and efficient model-agnostic framework tailored to amplify the capabilities of model-based and model-free network inference models.
Score: 13.0533106097336
License: http://creativecommons.org/licenses/by/4.0/
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 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.

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