Prediction and inference in complex networks: a brief review and perspectives
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912753977917440 |
|---|---|
| author | Rodrigues, Francisco A. |
| author_facet | Rodrigues, Francisco A. |
| contents | Inference and prediction are fundamental to the study of complex systems, where network data are often incomplete, inaccurate or obtained indirectly. In this paper, we review recent advances in network sampling and comparison, as well as in link prediction and network reconstruction from time series. We summarise key methodological developments and emerging approaches that integrate statistical and machine learning perspectives. We also outline promising research directions for enhancing the inference and prediction of complex networked systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07439 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prediction and inference in complex networks: a brief review and perspectives Rodrigues, Francisco A. Statistical Mechanics Physics and Society Inference and prediction are fundamental to the study of complex systems, where network data are often incomplete, inaccurate or obtained indirectly. In this paper, we review recent advances in network sampling and comparison, as well as in link prediction and network reconstruction from time series. We summarise key methodological developments and emerging approaches that integrate statistical and machine learning perspectives. We also outline promising research directions for enhancing the inference and prediction of complex networked systems. |
| title | Prediction and inference in complex networks: a brief review and perspectives |
| topic | Statistical Mechanics Physics and Society |
| url | https://arxiv.org/abs/2512.07439 |