From the time series to the complex networks: The parametric natural visibility graph

Bezsudnov, Igor V.; Snarskiı̆, A. A.

doi:10.1016/j.physa.2014.07.002

Cited by 85 publications

(44 citation statements)

References 38 publications

(41 reference statements)

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“…The visibility graph was proposed by Bezsudnov [32] and used to transform the time series to graphs, then these graphs are regarded as vertices to construct a new network [33]. Here, we choose five timing points from t to t + 4 and set every timing point as one vertex in the network, if two elements (e.g., with height being the value of AQI) at different timing points are visible, they will be connected in the network (see Fig.…”

Section: A the Process And Properties Of Visibility Graphmentioning

confidence: 99%

Chinese cities’ air quality pattern and correlation

Zhang

Guan

et al. 2020

J. Stat. Mech.

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Air quality impacts people's health and daily life, affects the sensitive ecosystems, and even restrains a country's development. By collecting and processing the time series data of Air Quality Index (AQI) of 363 cities of China from Jan. 2015 to Mar. 2019, we dedicated to characterize the universal patterns, the clustering and correlation of air quality of different cities by using the methods of complex network and time series analysis. The main results are as follows: 1) The Air Quality Network of China (AQNC) is constructed by using the Planar Maximally Filtered Graph (PMFG) method. The geographical distances on the correlation of air quality of different cities have been studied, it is found that 100 km is a critical distance for strong correlation. 2) Seven communities of AQNC have been detected, and their patterns have been analyzed by taking into account the Hurst exponent and climate environment, it is shown that the seven communities are reasonable, and they are significantly influenced by the climate factors, such as monsoon, precipitation, geographical regions, etc. 3) The motifs of air quality time series of seven communities have been investigated by the visibility graph, for some communities, the evolutionary patterns of the motifs are a bit stable, and they have the long-term memory effects. While for others, there are no stable patterns.

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Section: A the Process And Properties Of Visibility Graphmentioning

confidence: 99%

Chinese cities’ air quality pattern and correlation

Zhang

Guan

et al. 2020

J. Stat. Mech.

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“…cutoffs/thresholds) (Liu et al, 2015). VGs have been widely applied in many fields (Supriya et al, 2016;Stephen et al, 2015;Bezsudnov and Snarskii, 2014). However, as we discussed above, the VG has two disadvantages: first, it does not consider the effect of uneven sampling; second, it cannot capture the time series changes below a zero baseline.…”

Section: Introductionmentioning

confidence: 99%

Visibility Graph Based Community Detection for Biological Time Series

Min

Domanskyi

Piermarocchi

et al. 2020

Preprint

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MotivationTemporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems: (i) How to create the appropriate network to reflect the characteristics of biological time series. (ii) How to detect characteristic temporal patterns or events as network communities. General methods to detect communities have used metrics to compare the connectivity within a community to the connectivity one would expect in a random model, or assumed a known number of communities, or are based on the betweenness centrality of edges or nodes. However, such methods were not specifically designed for network representations of time series. We introduce a visibility-graph-based method to build networks from different kinds of biological time series and detect temporal communities within these networks.ResultsTo characterize the uneven sampling of typical experimentally obtained biological time series, and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG) for time series. To detect communities, we first find the shortest path of the network between start and end nodes to identify nodes which have high intensities. This identifies the main stem of our community detection algorithm. Then, we aggregate nodes outside the shortest path to the nodes found on the main stem based on the closest path length. Through simulation, we demonstrate the validity of our method in detecting community structures on various networks derived from simulated time series. We also confirm its effectiveness in revealing temporal communities in experimental biological time series. Our results suggest our method of visibility graph based community detection can be effective in detecting temporal biological patterns.AvailabilityThe methods of building WDPVG and visibility graph based community detection are available as a module of the open source Python package PyIOmica (https://doi.org/10.5281/zenodo.3691912) with documentation at https://pyiomica.readthedocs.io/en/latest/. The dataset and codes we used in this manuscript are publicly available at https://doi.org/10.5281/zenodo.3693984.Contactgmias@msu.edu

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“…Recently, there has been a growing industry in the application of complex network theory to carry out time series analysis [23,24]. The time series firstly is transformed into networks and then analyzed with various complex network tools [25][26][27][28]. Shirazi et al [29] demonstrated that the time series can be reconstructed with high precision by means of a simple random walk on their corresponding networks.…”

Section: Introductionmentioning

confidence: 99%

Construction of Complex Network with Multiple Time Series Relevance

Huang

Wang³

et al. 2018

Information

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Multivariate time series data, which comprise a set of ordered observations for multiple variables, are pervasively generated in weather conditions, traffic, financial stocks, etc. Therefore, it is of great significance to analyze the correlation between multiple time series. Financial stocks generate a significant amount of multivariate time series data that can be used to build networks that reflect market behavior. However, traditional commercial complex networks cannot fully utilize the multiple attributes of stocks and redundant filter relationships and reveal a more authentic financial stock market. We propose a fusion similarity of multiple time series and construct a threshold network with similarity. Furthermore, we define the connectivity efficiency to choose the best threshold, establishing a high connectivity efficiency network with the optimal network threshold. By searching the central node in the threshold network, we have found that the network center nodes constructed by our proposed method have a more comprehensive industry coverage than the traditional techniques to build the systems, and this also proves the superiority of this method.

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From the time series to the complex networks: The parametric natural visibility graph

Cited by 85 publications

References 38 publications

Chinese cities’ air quality pattern and correlation

Chinese cities’ air quality pattern and correlation

Visibility Graph Based Community Detection for Biological Time Series

Construction of Complex Network with Multiple Time Series Relevance

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