Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.02050 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910434435530752 |
|---|---|
| author | Wu, Kai Li, Yuanyuan Liu, Jing |
| author_facet | Wu, Kai Li, Yuanyuan Liu, Jing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_02050 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Machine learning of network inference enhancement from noisy measurements Wu, Kai Li, Yuanyuan Liu, Jing Social and Information Networks Machine Learning 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. |
| title | Machine learning of network inference enhancement from noisy measurements |
| topic | Social and Information Networks Machine Learning |
| url | https://arxiv.org/abs/2309.02050 |