Complex Network Theory in the Application of Optimization Topology Network

2014 ◽  
Vol 651-653 ◽  
pp. 1811-1815 ◽  
Author(s):  
Bao Kun An ◽  
Yan Feng
Keyword(s):  
Complex Network ◽  
Network Topology ◽  
Network Theory ◽  
Computer Network ◽  
Rapid Development ◽  
Network Models ◽  
Complex Network Theory ◽  
New Development ◽  
The Times ◽  
Complex Network Models

Complex network theory is a new theory, which is rising with the rapid development of the computer. At present, the network structure of computers more complex network models already existed, has been unable to meet its topological properties. So, emerge as the times require complex network theory, provides a new development ideas and platform at the same time, complex network theory to the study of computer network topology. This paper firstly introduces the complex network theory, and then about the application of complex network theory in computer topology behavior, research on computer network topology and the experiment and model specific about complex network theory. More reduction.

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.

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.

A framework for assessing project vulnerability to crises

2019 ◽  
Vol 12 (4) ◽  
pp. 1079-1096 ◽  
Author(s):  
Aimin Wang
Keyword(s):  
Complex Network ◽  
Network Topology ◽  
Network Theory ◽  
Critical Role ◽  
Diffusion Path ◽  
Content Type ◽  
Complex Network Theory ◽  
Systems Perspective ◽  
Project Network

Purpose The purpose of this paper is to propose a framework for assessing the vulnerability of projects to crises. The study seeks to clarify the cascade effects of disruptions leading to project crises and to improve project robustness against crises from a systems perspective. Design/methodology/approach A framework for assessing project vulnerability to crises is developed using complex network theory. The framework includes network representation of project systems, analyzing project network topology, simulating the cascade of unexpected disruptions and assessing project vulnerability. Use of the framework is then illustrated by applying it to a case study of a construction project. Findings Project network topology plays a critical role in resisting crises. By increasing the resilience of the critical tasks and adjusting the structure of a project, the complexity and vulnerability of the project can be reduced, which in turn decreases the occurrence of crises. Research limitations/implications The proposed framework is used in a case study. Further studies of its application to projects in diverse industries would be beneficial to enhance the robustness of the results. Practical implications Project crises can threaten the survival of a project and endanger the organization’s security. The proposed framework helps prevent and mitigate project crises by protecting critical tasks and blocking the diffusion path from a systems perspective. Originality/value This paper presents a novel framework based on complex network theory to assess project vulnerability, which provides a systemic understanding of the cascade of disruptions that lead to project crises.

scholarly journals Impact of Network Topology on the Spread of Infectious Diseases

2020 ◽  
Vol 21 (1) ◽  
pp. 95
Author(s):  
Eduardo R. Pinto ◽  
Erivelton G. Nepomuceno ◽  
Andriana S. L. O. Campanharo
Keyword(s):  
Infectious Disease ◽  
Complex Network ◽  
Network Models ◽  
Vaccination Strategy ◽  
Small World ◽  
Disease Spread ◽  
Complex Network Theory ◽  
General Complex ◽  
The Individual ◽  
Complex Network Models

The complex network theory constitutes a natural support for the study of a disease propagation. In this work, we present a study of an infectious disease spread with the use of this theory in combination with the Individual Based Model. More specifically, we use several complex network models widely known in the literature to verify their topological effects in the propagation of the disease. In general, complex networks with different properties result in curves of infected individuals with different behaviors, and thus, the growth of a given disease is highly sensitive to the network model used. The disease eradication is observed when the vaccination strategy of 10% of the population is used in combination with the random, small world or modular network models, which opens an important space for control actions that focus on changing the topology of a complex network as a form of reduction or even elimination of an infectious disease.

Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

2021 ◽  
pp. 1063293X2110031
Author(s):  
Maolin Yang ◽  
Auwal H Abubakar ◽  
Pingyu Jiang
Keyword(s):  
Deep Learning ◽  
Complex Network ◽  
Network Models ◽  
Neural Network Models ◽  
Complex Network Theory ◽  
New Type ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

scholarly journals Ecological Network Optimization in Urban Central District Based on Complex Network Theory: A Case Study with the Urban Central District of Harbin

2021 ◽  
Vol 18 (4) ◽  
pp. 1427
Author(s):  
Shuang Song ◽  
Dawei Xu ◽  
Shanshan Hu ◽  
Mengxi Shi
Keyword(s):  
Complex Network ◽  
Network Optimization ◽  
Network Theory ◽  
Habitat Destruction ◽  
Landscape Patterns ◽  
Ecological Network ◽  
Complex Network Theory ◽  
Ecological Corridors ◽  
Low Degree ◽  
Central District

Habitat destruction and declining ecosystem service levels caused by urban expansion have led to increased ecological risks in cities, and ecological network optimization has become the main way to resolve this contradiction. Here, we used landscape patterns, meteorological and hydrological data as data sources, applied the complex network theory, landscape ecology, and spatial analysis technology, a quantitative analysis of the current state of landscape pattern characteristics in the central district of Harbin was conducted. The minimum cumulative resistance was used to extract the ecological network of the study area. Optimized the ecological network by edge-adding of the complex network theory, compared the optimizing effects of different edge-adding strategies by using robustness analysis, and put forward an effective way to optimize the ecological network of the study area. The results demonstrate that: The ecological patches of Daowai, Xiangfang, Nangang, and other old districts in the study area are small in size, fewer in number, strongly fragmented, with a single external morphology, and high internal porosity. While the ecological patches in the new districts of Songbei, Hulan, and Acheng have a relatively good foundation. And ecological network connectivity in the study area is generally poor, the ecological corridors are relatively sparse and scattered, the connections between various ecological sources of the corridors are not close. Comparing different edge-adding strategies of complex network theory, the low-degree-first strategy has the most outstanding performance in the robustness test. The low-degree-first strategy was used to optimize the ecological network of the study area, 43 ecological corridors are added. After the optimization, the large and the small ecological corridors are evenly distributed to form a complete network, the optimized ecological network will be significantly more connected, resilient, and resistant to interference, the ecological flow transmission will be more efficient.

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