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.

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.

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 Regulating heat conduction of complex networks by distributed nodes masses

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kezhao Xiong ◽  
Zhengxin Yan ◽  
You Xie ◽  
Zonghua Liu
Keyword(s):  
Heat Conduction ◽  
Temperature Distribution ◽  
Complex Networks ◽  
Complex Network ◽  
Heat Dissipation ◽  
Waste Heat ◽  
Challenging Problem ◽  
Potential Implication ◽  
Controlling Parameter ◽  
Network Topologies

AbstractDeveloping efficient strategy to regulate heat conduction is a challenging problem, with potential implication in the field of thermal materials. We here focus on a potential thermal material, i.e. complex networks of nanowires and nanotubes, and propose a model where the mass of each node is assigned proportional to its degree with $$m_i\sim k_i^{\alpha }$$ m i ∼ k i α , to investigate how distributed nodes masses can impact the heat flow in a network. We find that the heat conduction of complex network can be either increased or decreased, depending on the controlling parameter $$\alpha$$ α . Especially, there is an optimal heat conduction at $$\alpha =1$$ α = 1 and it is independent of network topologies. Moreover, we find that the temperature distribution within a complex network is also strongly influenced by the controlling parameter $$\alpha$$ α . A brief theoretical analysis is provided to explain these results. These findings may open up appealing applications in the cases of demanding either increasing or decreasing heat conduction, and our approach of regulating heat conduction by distributed nodes masses may be also valuable to the challenge of controlling waste heat dissipation in highly integrated and miniaturized modern devices.

scholarly journals A detailed characterization of complex networks using Information Theory

2021 ◽  
Author(s):  
CGS Freitas ◽  
ALL Aquino ◽  
HS Ramos ◽  
Alejandro Frery ◽  
OA Rosso
Keyword(s):  
Information Theory ◽  
Complex Networks ◽  
Complex Network ◽  
Preferential Attachment ◽  
Configuration Model ◽  
Complex Network Theory ◽  
Scale Free ◽  
Network Entropy ◽  
Law Degree

Understanding the structure and the dynamics of networks is of paramount importance for many scientific fields that rely on network science. Complex network theory provides a variety of features that help in the evaluation of network behavior. However, such analysis can be confusing and misleading as there are many intrinsic properties for each network metric. Alternatively, Information Theory methods have gained the spotlight because of their ability to create a quantitative and robust characterization of such networks. In this work, we use two Information Theory quantifiers, namely Network Entropy and Network Fisher Information Measure, to analyzing those networks. Our approach detects non-trivial characteristics of complex networks such as the transition present in the Watts-Strogatz model from k-ring to random graphs; the phase transition from a disconnected to an almost surely connected network when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-free networks when considering a non-linear preferential attachment, fitness, and aging features alongside the configuration model with a pure power-law degree distribution. Finally, we analyze the numerical results for real networks, contrasting our findings with traditional complex network methods. In conclusion, we present an efficient method that ignites the debate on network characterization.

scholarly journals Combining complex networks and data mining: why and how

2016 ◽  
Author(s):  
M. Zanin ◽  
D. Papo ◽  
P. A. Sousa ◽  
E. Menasalvas ◽  
A. Nicchi ◽  
...  
Keyword(s):  
Data Mining ◽  
Complex Networks ◽  
Complex Network ◽  
Network Theory ◽  
Data Sets ◽  
Complex Network Theory ◽  
Data Mining Algorithms ◽  
Starting Point ◽  
The Face ◽  
General Goal

AbstractThe increasing power of computer technology does not dispense with the need to extract meaningful in-formation out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex network metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.

scholarly journals Study on Knowledge Propagation in Complex Networks Based on Preferences, Taking Wechat as Example

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Si-hua Chen ◽  
Wei He
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Network Theory ◽  
Simulation Experiment ◽  
Propagation Model ◽  
Traditional Media ◽  
Complex Network Theory ◽  
Knowledge Propagation

As platform based on users’ relationship to acquire, share, and propagate knowledge, Wechat develops very rapidly and becomes an important channel to spread knowledge. This new way to propagate knowledge is quite different from the traditional media way which enables knowledge to be spread surprisingly in Wechat. Based on complex network theory and the analysis of the factors which influence the knowledge propagation in Wechat, this paper summarizes the behavior preferences of Wechat users in knowledge propagation and establishes a Wechat knowledge propagation model. By the simulation experiment, this paper tests the model established and finds some important thresholds in knowledge propagation in Wechat. The findings are valuable for further studying the knowledge propagation in Wechat and provide theoretical proof for forecasting the scale and influence of knowledge propagation.

scholarly journals A detailed characterization of complex networks using Information Theory

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Cristopher G. S. Freitas ◽  
Andre L. L. Aquino ◽  
Heitor S. Ramos ◽  
Alejandro C. Frery ◽  
Osvaldo A. Rosso
Keyword(s):  
Information Theory ◽  
Complex Networks ◽  
Complex Network ◽  
Preferential Attachment ◽  
Configuration Model ◽  
Complex Network Theory ◽  
Scale Free ◽  
Network Entropy ◽  
Law Degree

Abstract Understanding the structure and the dynamics of networks is of paramount importance for many scientific fields that rely on network science. Complex network theory provides a variety of features that help in the evaluation of network behavior. However, such analysis can be confusing and misleading as there are many intrinsic properties for each network metric. Alternatively, Information Theory methods have gained the spotlight because of their ability to create a quantitative and robust characterization of such networks. In this work, we use two Information Theory quantifiers, namely Network Entropy and Network Fisher Information Measure, to analyzing those networks. Our approach detects non-trivial characteristics of complex networks such as the transition present in the Watts-Strogatz model from k-ring to random graphs; the phase transition from a disconnected to an almost surely connected network when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-free networks when considering a non-linear preferential attachment, fitness, and aging features alongside the configuration model with a pure power-law degree distribution. Finally, we analyze the numerical results for real networks, contrasting our findings with traditional complex network methods. In conclusion, we present an efficient method that ignites the debate on network characterization.

scholarly journals A detailed characterization of complex networks using Information Theory

2021 ◽  
Author(s):  
CGS Freitas ◽  
ALL Aquino ◽  
HS Ramos ◽  
Alejandro Frery ◽  
OA Rosso
Keyword(s):  
Information Theory ◽  
Complex Networks ◽  
Complex Network ◽  
Preferential Attachment ◽  
Configuration Model ◽  
Complex Network Theory ◽  
Scale Free ◽  
Network Entropy ◽  
Law Degree

Understanding the structure and the dynamics of networks is of paramount importance for many scientific fields that rely on network science. Complex network theory provides a variety of features that help in the evaluation of network behavior. However, such analysis can be confusing and misleading as there are many intrinsic properties for each network metric. Alternatively, Information Theory methods have gained the spotlight because of their ability to create a quantitative and robust characterization of such networks. In this work, we use two Information Theory quantifiers, namely Network Entropy and Network Fisher Information Measure, to analyzing those networks. Our approach detects non-trivial characteristics of complex networks such as the transition present in the Watts-Strogatz model from k-ring to random graphs; the phase transition from a disconnected to an almost surely connected network when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-free networks when considering a non-linear preferential attachment, fitness, and aging features alongside the configuration model with a pure power-law degree distribution. Finally, we analyze the numerical results for real networks, contrasting our findings with traditional complex network methods. In conclusion, we present an efficient method that ignites the debate on network characterization.

scholarly journals Complex Network Theory and Its Application Research on P2P Networks

2016 ◽  
Vol 1 (1) ◽  
pp. 45-52 ◽  
Author(s):  
Feng Jian ◽  
Shi Dandan
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Graph Model ◽  
Clustering Coefficient ◽  
Research Network ◽  
P2p Networks ◽  
Simple Description ◽  
Random Graph Model ◽  
Complex Network Theory ◽  
Network Mechanism

AbstractAdvances in complex networks of Peer-to-Peer (P2P) networks were reviewed and summarized. The paper outlines some important topological properties such as degree, average path length and clustering coefficient at first, and then three kinds of most important network mechanism models are introduced, including random graph model, small world model and scale-free model. A simple description about research status for P2P networks based on complex networks is made from three aspects: positive research, network mechanism model, network broadcast and control. Some developing prospects of complex networks of P2P are pointed out finally. Complex network provides new ideas and methods to deal with many complex problems including P2P networks.

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>

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