Global Reconstruction of Complex Network Topology via Structured Compressive Sensing

2020 ◽  
pp. 1-11
Author(s):  
Jingchao Dai ◽  
Keke Huang ◽  
Yishun Liu ◽  
Chunhua Yang ◽  
Zhen Wang
Keyword(s):  
Compressive Sensing ◽  
Complex Network ◽  
Network Topology ◽  
Global Reconstruction

Modeling and Simulation for Complex Network Based Military System-of-Systems (SoS)

2011 ◽  
Vol 145 ◽  
pp. 224-228 ◽  
Author(s):  
Xiao Song ◽  
Bing Cheng Liu ◽  
Guang Hong Gong
Keyword(s):  
Modeling And Simulation ◽  
Complex Network ◽  
Network Topology ◽  
Network Theory ◽  
Small World ◽  
System Of Systems ◽  
Statistical Parameters ◽  
Complex Network Theory ◽  
Scale Free ◽  
Topology Model

Military SoS increasingly shows its relation of complex network. According to complex network theory, we construct a SoS network topology model for network warfare simulation. Analyzing statistical parameters of the model, it is concluded that the topology model has small-world, high-aggregation and scale-free properties. Based on this model we mainly simulate and analyze vulnerability of the network. And this provides basis for analysis of the robustness and vulnerability of real battle SoS network.

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.

AN IDEAL DISASSORTATIVE NETWORK AND SYNCHRONIZATION

2006 ◽  
Vol 16 (10) ◽  
pp. 3093-3102 ◽  
Author(s):  
DONG CHENGDONG ◽  
LIU ZENGRONG
Keyword(s):  
Dynamic System ◽  
Complex Network ◽  
Network Topology ◽  
Path Length ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Novel Complex ◽  
The Impact

This paper proposes a novel complex network with disassortative property based on multicenter networks. The average path length and clustering coefficient of the network are calculated, and the impact on the network topology is investigated. A simple dynamic system established on the proposed network is used to analyze how the disassortative property of the network affects synchronization.

scholarly journals Connective Core Structures in Cognitive Networks: the Role of Hubs

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 961 ◽  
Author(s):  
Carlos Arruda Arruda Baltazar ◽  
Maria Isabel Barros Guinle ◽  
Cora Jirschik Caron ◽  
Edson Amaro ◽  
Birajara Soares Machado
Keyword(s):  
Complex Network ◽  
Network Topology ◽  
Information Integration ◽  
Effective Connectivity ◽  
Cognitive Networks ◽  
Invariant Structure ◽  
Network Effectiveness ◽  
Efficient Information ◽  
Behavioral States

Complex network analysis applied to the resting brain has shown that sets of highly interconnected networks with coherent activity may support a default mode of brain function within a global workspace. Perceptual processing of environmental stimuli induces architectural changes in network topology with higher specialized modules. Evidence shows that during cognitive tasks, network topology is reconfigured and information is broadcast from modular processors to a connective core, promoting efficient information integration. In this paper, we explored how the brain adapts its effective connectivity within the connective core and across behavioral states. We used complex network metrics to identify hubs and proposed a method of classification based on the effective connectivity patterns of information flow. Finally, we interpreted the role of the connective core and each type of hub on the network effectiveness. We also calculated the complexity of electroencephalography microstate sequences across different tasks. We observed that divergent hubs contribute significantly to the network effectiveness and that part of this contribution persists across behavioral states, forming an invariant structure. Moreover, we found that a large quantity of multiple types of hubs may be associated with transitions of functional networks.

Decluster: a complex network model-based data center network topology

2014 ◽  
Vol 70 (3) ◽  
pp. 1365-1382
Author(s):  
Xu Zhang ◽  
Hai Wang ◽  
Qingyuan Gong ◽  
Xin Wang
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Network Topology ◽  
Data Center ◽  
Data Center Network ◽  
Model Based

scholarly journals A Cascade-Based Emergency Model for Water Distribution Network

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Qing Shuang ◽  
Yongbo Yuan ◽  
Mingyuan Zhang ◽  
Yisheng Liu
Keyword(s):  
Complex Network ◽  
Network Topology ◽  
Water Distribution ◽  
Distribution Network ◽  
Water Distribution Network ◽  
Topology Analysis ◽  
Physical Infrastructure ◽  
Recovery Strategies ◽  
Recovery Mechanisms ◽  
Redistribution Function

Water distribution network is important in the critical physical infrastructure systems. The paper studies the emergency resource strategies on water distribution network with the approach of complex network and cascading failures. The model of cascade-based emergency for water distribution network is built. The cascade-based model considers the network topology analysis and hydraulic analysis to provide a more realistic result. A load redistribution function with emergency recovery mechanisms is established. From the aspects of uniform distribution, node betweenness, and node pressure, six recovery strategies are given to reflect the network topology and the failure information, respectively. The recovery strategies are evaluated with the complex network indicators to describe the failure scale and failure velocity. The proposed method is applied by an illustrative example. The results showed that the recovery strategy considering the node pressure can enhance the network robustness effectively. Besides, this strategy can reduce the failure nodes and generate the least failure nodes per time.

scholarly journals Small-World and Scale-Free Network Models for IoT Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Insoo Sohn
Keyword(s):  
Graph Theory ◽  
Complex Network ◽  
Network Topology ◽  
Network Models ◽  
Small World ◽  
Small World Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
Massive Number ◽  
Complex Network Models

It is expected that Internet of Things (IoT) revolution will enable new solutions and business for consumers and entrepreneurs by connecting billions of physical world devices with varying capabilities. However, for successful realization of IoT, challenges such as heterogeneous connectivity, ubiquitous coverage, reduced network and device complexity, enhanced power savings, and enhanced resource management have to be solved. All these challenges are heavily impacted by the IoT network topology supported by massive number of connected devices. Small-world networks and scale-free networks are important complex network models with massive number of nodes and have been actively used to study the network topology of brain networks, social networks, and wireless networks. These models, also, have been applied to IoT networks to enhance synchronization, error tolerance, and more. However, due to interdisciplinary nature of the network science, with heavy emphasis on graph theory, it is not easy to study the various tools provided by complex network models. Therefore, in this paper, we attempt to introduce basic concepts of graph theory, including small-world networks and scale-free networks, and provide system models that can be easily implemented to be used as a powerful tool in solving various research problems related to IoT.

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