A novel approach for converting spatio-temporal series into complex networks
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911009422180352 |
|---|---|
| author | Yalcin, G. Cigdem Onder, M. Berk |
| author_facet | Yalcin, G. Cigdem Onder, M. Berk |
| contents | This study aims to offer a new perspective on complex network representation of real-world systems. Currently, the most well-known transformation algorithms in the literature treat each data point in a time series as a node and transform the time series into a network. In this study, we present a new approach converting spatio-temporal series into a complex network. We focus on studying this transformation by grounding it in the context of physics , with the aim of adapting it to real-world problems, which often manifest as complex systems across various domains. We introduce the Gravitational Graph (GG) algorithm, which is grounded in the concept of gravitational force from fundamental physics. We consider air pollution concentrations, which represent a global environmental health risk, as an example of a complex environmental system, and apply the GG algorithm to particulate matter of 10 microns (PM10) recorded by 21 air quality monitoring stations located in various regions of Istanbul, Turkiye. While the GG algorithm allows for the conversion of spatio-temporal series -- rather than time series -- into networks, it also enables the analysis of the statistical properties and characteristics of the converted networks, thereby uncovering hidden relationships and dependencies that may not be apparent in the original time series. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A novel approach for converting spatio-temporal series into complex networks Yalcin, G. Cigdem Onder, M. Berk Physics and Society Disordered Systems and Neural Networks Statistical Mechanics This study aims to offer a new perspective on complex network representation of real-world systems. Currently, the most well-known transformation algorithms in the literature treat each data point in a time series as a node and transform the time series into a network. In this study, we present a new approach converting spatio-temporal series into a complex network. We focus on studying this transformation by grounding it in the context of physics , with the aim of adapting it to real-world problems, which often manifest as complex systems across various domains. We introduce the Gravitational Graph (GG) algorithm, which is grounded in the concept of gravitational force from fundamental physics. We consider air pollution concentrations, which represent a global environmental health risk, as an example of a complex environmental system, and apply the GG algorithm to particulate matter of 10 microns (PM10) recorded by 21 air quality monitoring stations located in various regions of Istanbul, Turkiye. While the GG algorithm allows for the conversion of spatio-temporal series -- rather than time series -- into networks, it also enables the analysis of the statistical properties and characteristics of the converted networks, thereby uncovering hidden relationships and dependencies that may not be apparent in the original time series. |
| title | A novel approach for converting spatio-temporal series into complex networks |
| topic | Physics and Society Disordered Systems and Neural Networks Statistical Mechanics |
| url | https://arxiv.org/abs/2506.14378 |