scholarly journals The Effect of Anti-COVID-19 Policies on the Evolution of the Disease: A Complex Network Analysis of the Successful Case of Greece

Physics ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 325-339 ◽  
Author(s):  
Dimitrios Tsiotas ◽  
Lykourgos Magafas
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Complex Network ◽  
Graph Algorithm ◽  
Visibility Graph ◽  
Successful Case ◽  
Power Pattern ◽  
Novel Method ◽  
Good Policy ◽  
Of Greece

Within the context of Greece promising a success story in the fight against the disease, this paper proposes a novel method for studying the evolution of the Greek COVID-19 infection curve in relation to the anti-COVID-19 policies applied to control the pandemic. Based on the ongoing spread of COVID-19 and the insufficient data for applying classic time-series approaches, the analysis builds on the visibility graph algorithm to study the Greek COVID-19 infection curve as a complex network. By using the modularity optimization algorithm, the generated visibility graph is divided into communities defining periods of different connectivity in the time-series body. These periods reveal a sequence of different typologies in the evolution of the disease, starting with a power pattern, where a second order polynomial (U-shaped) pattern intermediates, being followed by a couple of exponential patterns, and ending up with a current logarithmic pattern revealing that the evolution of the Greek COVID-19 infection curve tends towards saturation. In terms of Gaussian modeling, this successive compression of the COVID-19 infection curve into five parts implies that the pandemic in Greece is about to reach the second (decline) half of the bell-shaped distribution. The network analysis also illustrates stability of hubs and instability of medium and low-degree nodes, implying a low probability of meeting maximum (infection) values in the future and high uncertainty in the variability of other values below the average. The overall approach contributes to the scientific research by proposing a novel method for the structural decomposition of a time-series into periods, which allows removing from the series the disconnected past-data facilitating better forecasting, and provides insights of good policy and decision-making practices and management that may help other countries improve their performance in the war against COVID-19.

scholarly journals Examination of Chaotic Structures in Semiconductor or Alloy Voltage Time-Series: A Complex Network Approach for the Case of TlInTe2

Physics ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 624-639
Author(s):  
Dimitrios Tsiotas ◽  
Lykourgos Magafas ◽  
Michael P. Hanias
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Complex Network ◽  
Graph Algorithm ◽  
Differential Resistance ◽  
Visibility Graph ◽  
Characteristic Region ◽  
Scale Free ◽  
Complex Network Analysis ◽  
Voltage Oscillation

This paper proposes a method for examining chaotic structures in semiconductor or alloy voltage oscillation time-series, and focuses on the case of the TlInTe2 semiconductor. The available voltage time-series are characterized by instabilities in negative differential resistance in the current–voltage characteristic region, and are primarily chaotic in nature. The analysis uses a complex network analysis of the time-series and applies the visibility graph algorithm to transform the available time-series into a graph so that the topological properties of the graph can be studied instead of the source time-series. The results reveal a hybrid lattice-like configuration and a major hierarchical structure corresponding to scale-free characteristics in the topology of the visibility graph, which is in accordance with the default hybrid chaotic and semi-periodic structure of the time-series. A novel conceptualization of community detection based on modularity optimization is applied to the available time-series and reveals two major communities that are able to be related to the pair-wise attractor of the voltage oscillations’ phase portrait of the TlInTe2 time-series. Additionally, the network analysis reveals which network measures are more able to preserve the chaotic properties of the source time-series. This analysis reveals metric information that is able to supplement the qualitative phase-space information. Overall, this paper proposes a complex network analysis of the time-series as a method for dealing with the complexity of semiconductor and alloy physics.

scholarly journals An electrostatics method for converting a time-series into a weighted complex network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dimitrios Tsiotas ◽  
Lykourgos Magafas ◽  
Panos Argyrakis
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Linear Trend ◽  
Weighted Graph ◽  
Graph Algorithm ◽  
Structural Similarity ◽  
Visibility Graph ◽  
Structural Relevance ◽  
Original Time ◽  
Electrically Charged

AbstractThis paper proposes a new method for converting a time-series into a weighted graph (complex network), which builds on electrostatics in physics. The proposed method conceptualizes a time-series as a series of stationary, electrically charged particles, on which Coulomb-like forces can be computed. This allows generating electrostatic-like graphs associated with time-series that, additionally to the existing transformations, can be also weighted and sometimes disconnected. Within this context, this paper examines the structural similarity between five different types of time-series and their associated graphs that are generated by the proposed algorithm and the visibility graph, which is currently the most popular algorithm in the literature. The analysis compares the source (original) time-series with the node-series generated by network measures (that are arranged into the node-ordering of the source time-series), in terms of a linear trend, chaotic behaviour, stationarity, periodicity, and cyclical structure. It is shown that the proposed electrostatic graph algorithm generates graphs with node-measures that are more representative of the structure of the source time-series than the visibility graph. This makes the proposed algorithm more natural rather than algebraic, in comparison with existing physics-defined methods. The overall approach also suggests a methodological framework for evaluating the structural relevance between the source time-series and their associated graphs produced by any possible transformation.

scholarly journals Extracting Correlations in Earthquake Time Series Using Visibility Graph Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Sumanta Kundu ◽  
Anca Opris ◽  
Yohei Yukutake ◽  
Takahiro Hatano
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Recent Observation ◽  
Visibility Graph ◽  
Graph Analysis ◽  
Common Belief ◽  
Complex Network Theory ◽  
Event Time ◽  
Visibility Graph Analysis ◽  
Present Understanding

Recent observation studies have revealed that earthquakes are classified into several different categories. Each category might be characterized by the unique statistical feature in the time series, but the present understanding is still limited due to their non-linear and non-stationary nature. Here we utilize complex network theory to shed new light on the statistical properties of earthquake time series. We investigate two kinds of time series, which are magnitude and inter-event time (IET), for three different categories of earthquakes: regular earthquakes, earthquake swarms, and tectonic tremors. Following the criterion of visibility graph, earthquake time series are mapped into a complex network by considering each seismic event as a node and determining the links. As opposed to the current common belief, it is found that the magnitude time series are not statistically equivalent to random time series. The IET series exhibit correlations similar to fractional Brownian motion for all the categories of earthquakes. Furthermore, we show that the time series of three different categories of earthquakes can be distinguished by the topology of the associated visibility graph. Analysis on the assortativity coefficient also reveals that the swarms are more intermittent than the tremors.

scholarly journals Traffic Flow Characteristics mining of Time Series Data Based on Phase Space Reconstruction Using Complex Networks

CONVERTER ◽  
2021 ◽  
pp. 146-161
Author(s):  
Xue Xing, Yaqi Zhai, Zhongtai Jiang, Xiaoyu LI
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Network Analysis ◽  
Traffic Flow ◽  
Complex Network ◽  
Degree Distribution ◽  
Data Flow ◽  
Flow Time ◽  
Phase Space Reconstruction ◽  
Traffic Data

Traffic flow time series is vital for mining the traditional statistical characteristics by using the theory of statistics and machine learning when its identity is a special time series. The network analysis of the traffic flow time series, who uses the complex network of time series analysis method, is designed to inquire into the special law of traffic flow time series which uses its visualization characteristics. Through the network analysis of traffic data flow, the connotation of traffic data flow can be revealed, and the relationship between all data and some data can be further studied. Therefore, it is constructed by combination with the phase space reconstruction theory. The phase space trajectory may be squeezed and the structure of attractor may change. We need to use C-C method to estimate the time delay according to the characteristics of integral parameters, and use G-P algorithm to estimate the embedding dimension to avoid it. This study can effectively reveal the motion law of the system. After constructing the complex network of traffic flow time series with various traffic parameters, the degree distribution, clustering coefficient and modularization of the representative critical threshold corresponding network are statistically analysed. The analysis results show that the new networked structure of traffic flow time series proposed in this study has strong advantages, and its core is phase space reconstruction, which can well reflect the information space of traffic dynamic fluctuation. The time series networking method based on phase space reconstruction has become a new approach to inquire into the characteristics of traffic flow time series. The degree distribution of the actual multi-traffic parameter time series construction network satisfies the characteristics of a Gaussian distribution. Their average clustering coefficients have attenuation characteristics, and their modularization degree is obvious.

scholarly journals Limited penetrable visibility graph for establishing complex network from time series

2012 ◽  
Vol 61 (3) ◽  
pp. 030506
Author(s):  
Zhou Ting-Ting ◽  
Jin Ning-De ◽  
Gao Zhong-Ke ◽  
Luo Yue-Bin
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Visibility Graph

Complex network analysis of time series

2016 ◽  
Vol 116 (5) ◽  
pp. 50001 ◽  
Author(s):  
Zhong-Ke Gao ◽  
Michael Small ◽  
Jürgen Kurths
Keyword(s):  
Time Series ◽  
Network Analysis ◽  
Complex Network ◽  
Complex Network Analysis ◽  
Analysis Of Time Series

scholarly journals Analyzing Levels of Concern About Joint Punishment for Dishonesty Using the Visibility Graph Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhiqiang Qu ◽  
Yujie Zhang ◽  
Fan Li
Keyword(s):  
Time Series ◽  
Topological Structure ◽  
Time Series Data ◽  
Graph Algorithm ◽  
Visibility Graph ◽  
Series Data ◽  
Scale Free ◽  
Central Node ◽  
Important Means ◽  
Search Index

Joint punishment for dishonesty is an important means of administrative regulation. This research analyzed the dynamic characteristics of time series data from the Baidu search index using the keywords “joint punishment for dishonesty” based on a visibility graph network. Applying a visibility graph algorithm, time series data from the Baidu Index was transformed into complex networks, with parameters calculated to analyze the topological structure. Results showed differences in the use of joint punishment for dishonesty in certain provinces by calculating the parameters of the time series network from January 1, 2020 to May 27, 2021; it was also shown that most of the networks were scale-free. Finally, the results of K-means clustering showed that the 31 provinces (excluding Hong Kong, Macao and Taiwan) can be divided into four types. Meanwhile, by analyzing the national Baidu Index data from 2020 to May 2021, the period of the time series data and the influence range of the central node were found.

DYNAMICAL ANALYSES USING VISIBILITIES IN FINANCIAL MARKETS

Fractals ◽  
2016 ◽  
Vol 24 (02) ◽  
pp. 1650016 ◽  
Author(s):  
SEUNGSIK MIN ◽  
KYUSEONG LIM ◽  
KI-HO CHANG ◽  
IL-HWAN PARK ◽  
KYUNGSIK KIM
Keyword(s):  
Time Series ◽  
Financial Markets ◽  
Statistical Method ◽  
Poisson Distribution ◽  
Power Law ◽  
Graph Algorithm ◽  
Visibility Graph ◽  
Financial Networks ◽  
Topological Properties ◽  
Network Metrics

In this paper, the network metrics are studied in a time series of the KOSPI and the KOSDAQ indices converting by the visibility graph algorithm. The degree distributions for the KOSPI and the KOSDAQ are proportional to a power law rather than the Poisson distribution. Since we mainly simulate and analyze the network metrics from the nodes and its links, our result cannot be found unambiguously to have universal and characteristic properties of statistical quantities via financial networks. Particularly, these topological properties may improve by implementing the statistical method and its technique from altered data of financial networks.

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