Finding Hidden Communities in Complex Networks from Chaotic Time Series

2016 ◽  
Vol 3 (2) ◽  
pp. 350
Author(s):  
Jiancheng Sun
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Complex Network ◽  
Chaotic Time Series ◽  
Topology Structure ◽  
Complex Network Theory ◽  
Single Vertex ◽  
Reconstructed Phase Space ◽  
Information Coefficient ◽  
Maximal Information Coefficient

Recent works show that complex network theory may be another powerful tool in time series analysis. In this paper, we construct complex networks from the chaotic time series with Maximal Information Coefficient (MIC). Each vector point in the reconstructed phase space is represented by a single vertex and edge determined by MIC. By using the Chua’s circuit system, we illustrate the potential of these complex network measures for the detection of the topology structure of the network. Comparing with the linear relationship measure, we find that the topology structure of the community with MIC reveals the hidden or implied correlation of the network.

scholarly journals Research on Invulnerability of Wireless Sensor Networks Based on Complex Network Topology Structure

2017 ◽  
Vol 13 (03) ◽  
pp. 100 ◽  
Author(s):  
Zhigang Zhao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Complex Networks ◽  
Complex Network ◽  
Network Survivability ◽  
Wireless Sensor ◽  
Font Size ◽  
Topology Structure ◽  
Complex Network Theory ◽  
Different Types

<p><span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"><span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;">For real-world wireless sensor networks (WSNs), the invulnerability of the network is very critical, because a cascading failure would cause a serious effect on the whole network performance. Network survivability is closely dependent on the topology structure of a network. In this paper, [Note: If you use "firstly," you need to add "secondly," "thirdly,"... "finally" throughout this paragraph; I don't see a need for this here] we meticulously study the topology characteristics of WSNs based on the complex network theory. According to scale-free and small-world features of complex networks, the nodes of WSNs are divided into different types, including common node, super node, and sink node. From the point of view of invulnerability in complex networks, the influence of different types of nodes on the sensor networks' invulnerability is analyzed. Simulation experiments show that adding super nodes to the WSNs would significantly improve network survivability.</span></span></p>

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 Complex Network Construction of Univariate Chaotic Time Series Based on Maximum Mean Discrepancy

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 142
Author(s):  
Jiancheng Sun
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Gaussian Mixture ◽  
Chaotic Time Series ◽  
Network Construction ◽  
Maximum Mean Discrepancy ◽  
Univariate Time Series ◽  
Dimensional Phase Space ◽  
The Common ◽  
The Lorenz System

The analysis of chaotic time series is usually a challenging task due to its complexity. In this communication, a method of complex network construction is proposed for univariate chaotic time series, which provides a novel way to analyze time series. In the process of complex network construction, how to measure the similarity between the time series is a key problem to be solved. Due to the complexity of chaotic systems, the common metrics is hard to measure the similarity. Consequently, the proposed method first transforms univariate time series into high-dimensional phase space to increase its information, then uses Gaussian mixture model (GMM) to represent time series, and finally introduces maximum mean discrepancy (MMD) to measure the similarity between GMMs. The Lorenz system is used to validate the correctness and effectiveness of the proposed method for measuring the similarity.

Characterizing time series of near-miss accidents in metro construction via complex network theory

2017 ◽  
Vol 98 ◽  
pp. 145-158 ◽  
Author(s):  
Cheng Zhou ◽  
Lieyun Ding ◽  
Miroslaw J. Skibniewski ◽  
Hanbin Luo ◽  
Shuangnan Jiang
Keyword(s):  
Time Series ◽  
Complex Network ◽  
Network Theory ◽  
Near Miss ◽  
Complex Network Theory

Application of Complex Network Theory in Computer Network Topology Optimization Research

2014 ◽  
Vol 989-994 ◽  
pp. 4237-4240
Author(s):  
Zhi Kun Wang
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Complex Network ◽  
Network Topology ◽  
Computer Network ◽  
Transportation Network ◽  
Power Grids ◽  
Research Network ◽  
Complex Network Theory ◽  
New Research

If we apply the system internal elements as nodes, and the relationship between the elements as connection, then the system form a network. If we put emphasis on the structure of the system and analyze the function of the system from the angle of structure, we’ll find that real network topology properties differ from previous research network, and has numerous nodes, which is called complex networks. In the real word, many complex systems can be basically described by the network, while the reality is that complex systems can be called as “complex network”, such as social network, transportation network, power grids and internet etc. In recent years, many articles about the complex networks are released in the international first-class publications such as Nature, PRL, PNAS, which reflects that the complex networks has become a new research focus.

scholarly journals Continuous Variables Graph States Shaped as Complex Networks: Optimization and Manipulation

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 26
Author(s):  
Francesca Sansavini ◽  
Valentina Parigi
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Quantum Algorithms ◽  
Minimal Amount ◽  
Continuous Variables ◽  
Linear Optics ◽  
Cluster States ◽  
Graph States ◽  
Complex Network Theory ◽  
Network Topologies

Complex networks structures have been extensively used for describing complex natural and technological systems, like the Internet or social networks. More recently, complex network theory has been applied to quantum systems, where complex network topologies may emerge in multiparty quantum states and quantum algorithms have been studied in complex graph structures. In this work, we study multimode Continuous Variables entangled states, named cluster states, where the entanglement structure is arranged in typical real-world complex networks shapes. Cluster states are a resource for measurement-based quantum information protocols, where the quality of a cluster is assessed in terms of the minimal amount of noise it introduces in the computation. We study optimal graph states that can be obtained with experimentally realistic quantum resources, when optimized via analytical procedure. We show that denser and regular graphs allow for better optimization. In the spirit of quantum routing, we also show the reshaping of entanglement connections in small networks via linear optics operations based on numerical optimization.

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