# Time Series Analysis via Network Science: Concepts and Algorithms

arXiv: https://arxiv.org/abs/2110.09887 · HTML: https://mlsanity.com/paper/2110.09887/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 172 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [The structure and dynamics of multilayer networks](https://mlsanity.com/paper/1407.0742/) (2014)
- [A Tutorial on Regularized Partial Correlation Networks](https://mlsanity.com/paper/1607.01367/) (2016)
- [Sequential visibility-graph motifs](https://mlsanity.com/paper/1512.00297/) (2015)
- [Multiplex visibility graphs to investigate recurrent neural networks dynamics](https://mlsanity.com/paper/1609.03068/) (2016)
- [Distinguishing noise from chaos: objective versus subjective criteria using Horizontal Visibility Graph](https://mlsanity.com/paper/1401.2139/) (2014)
- [Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time Series](https://mlsanity.com/paper/1610.07273/) (2016)
- [Efficient On-line Computation of Visibility Graphs](https://mlsanity.com/paper/1905.03204/) (2019)
- [Exact results of the limited penetrable horizontal visibility graph associated to random time series and its application](https://mlsanity.com/paper/1710.09877/) (2017)

## Built on by (most cited first, 8 of 8)

- [From Time Series to Networks in R with the ts2net Package](https://mlsanity.com/paper/2208.09660/) (2022)
- [Grasynda: Graph-based Synthetic Time Series Generation](https://mlsanity.com/paper/2601.19668/) (2026)
- [Attention-based graph neural networks: a survey](https://mlsanity.com/paper/2605.08679/) (2026)
- [A Network Science Approach to Granular Time Series Segmentation](https://mlsanity.com/paper/2505.17640/) (2025)
- [Fiaingen: A financial time series generative method matching real-world data quality](https://mlsanity.com/paper/2510.01169/) (2025)
- [Synthetic Time Series Generation via Complex Networks](https://mlsanity.com/paper/2601.22879/) (2026)
- [Graph2TS: Structure-Controlled Time Series Generation via Quantile-Graph VAEs](https://mlsanity.com/paper/2603.19970/) (2026)
- [Persistent Homology of Time Series through Complex Networks](https://mlsanity.com/paper/2605.01624/) (2026)

## Abstract (arXiv)

There is nowadays a constant flux of data being generated and collected in all types of real world systems. These data sets are often indexed by time, space or both requiring appropriate approaches to analyze the data. In univariate settings, time series analysis is a mature and solid field. However, in multivariate contexts, time series analysis still presents many limitations. In order to address these issues, the last decade has brought approaches based on network science. These methods involve transforming an initial time series data set into one or more networks, which can be analyzed in depth to provide insight into the original time series. This review provides a comprehensive overview of existing mapping methods for transforming time series into networks for a wide audience of researchers and practitioners in machine learning, data mining and time series. Our main contribution is a structured review of existing methodologies, identifying their main characteristics and their differences. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified notation and language. We first describe the case of univariate time series, which can be mapped to single layer networks, and we divide the current mappings based on the underlying concept: visibility, transition and proximity. We then proceed with multivariate time series discussing both single layer and multiple layer approaches. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic.