# Machine learning of network inference enhancement from noisy measurements

arXiv: https://arxiv.org/abs/2309.02050 · HTML: https://mlsanity.com/paper/2309.02050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 61 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)
- [Network structure from rich but noisy data](https://mlsanity.com/paper/1703.07376/) (2017)
- [Network reconstruction and community detection from dynamics](https://mlsanity.com/paper/1903.10833/) (2019)
- [Reconstructing networks with unknown and heterogeneous errors](https://mlsanity.com/paper/1806.07956/) (2018)
- [Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links](https://mlsanity.com/paper/1912.02721/) (2019)
- [Unifying Pairwise Interactions in Complex Dynamics](https://mlsanity.com/paper/2201.11941/) (2022)
- [Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests](https://mlsanity.com/paper/2010.15289/) (2020)
- [Robust Reconstruction of Complex Networks from Sparse Data](https://mlsanity.com/paper/1501.04731/) (2015)
- [Reconstructing propagation networks with natural diversity and identifying hidden sources](https://mlsanity.com/paper/1407.4451/) (2014)
- [Estimating network structure from unreliable measurements](https://mlsanity.com/paper/1803.02427/) (2018)

## Abstract (arXiv)

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.