scholarly journals Topological Characteristics and Vulnerability Analysis of Rural Traffic Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Xia Zhu ◽  
Weidong Song ◽  
Lin Gao
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Network Models ◽  
Mapping Method ◽  
Traffic Network ◽  
Complex Network Theory ◽  
The Road ◽  
Topological Features ◽  
Attack Patterns ◽  
Road Mapping

Rural traffic network (RTN), as a complex network, plays a significant role in the field of resisting natural disasters and emergencies. In this paper, we analyze the vulnerability of RTN via three traffic network models (i.e., No-power Traffic Network Model (NTNM), Distance Weight Traffic Network Model (DWTNM), and Road Level Weight Traffic Network Model (RLWTNM)). Firstly, based on the complex network theory, RTN is constructed by using road mapping method, according to the topological features. Secondly, Random Attack (RA) and Deliberate Attack (DA) strategies are used to analyze network vulnerability in three rural traffic network models. By analyzing the attack tolerance of RTN under the condition of different attack patterns, we find that the road level weight traffic network has a good performance to represent the vulnerability of RTN.

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 Regional Patch Detection of Road Traffic Network

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Xia Zhu ◽  
Weidong Song ◽  
Lin Gao
Keyword(s):  
Complex Network ◽  
Road Traffic ◽  
Geographic Distance ◽  
Influence Factors ◽  
Impact Factors ◽  
Traffic Network ◽  
The Road ◽  
Key Points ◽  
Path Distance ◽  
Shortest Path Distance

Road traffic network (RTN) structure plays an important role in the field of complex network analysis. In this paper, we propose a regional patch detection method from RTN via community detection of complex network. Firstly, the refined Adapted PageRank algorithm, which combines with the influence factors of the location property weight, the geographic distance weight and the road level weight, is used to calculate the candidate ranking results of key nodes in the RTN. Secondly, the ranking result and the shortest path distance as two significant impact factors are used to select the key points of the RTN, and then the Adapted K-Means algorithm is applied to regional patch detection of the RTN. Finally, based on the experimental data of Zhangwu road traffic network, the analysis results are as follows: Zhangwu is divided into 9 functional structures with key node locations as the core. Regional patch structure is divided according to key points, and the RTN is actually divided into nine small functional communities. Nine functional regional patches constitute a new network structure, maintaining connectivity between the regional patches can improve the overall efficiency of the RTN.

scholarly journals Complex network and fractal theory for the assessment of water distribution network resilience to pipe failures

2017 ◽  
Vol 18 (3) ◽  
pp. 767-777 ◽  
Author(s):  
Armando Di Nardo ◽  
Michele Di Natale ◽  
Carlo Giudicianni ◽  
Roberto Greco ◽  
Giovanni Francesco Santonastaso
Keyword(s):  
Complex Network ◽  
Water Distribution ◽  
Fractal Theory ◽  
Distribution Networks ◽  
Pipe Failure ◽  
Operational Conditions ◽  
Complex Network Theory ◽  
Topological Features ◽  
Wide Range ◽  
The Impact

AbstractWater distribution networks (WDNs) must keep a proper level of service under a wide range of operational conditions, and, in particular, the analysis of their resilience to pipe failures is essential to improve their design and management. WDNs can be regarded as large sparse planar graphs showing fractal and complex network properties. In this paper, the relationship linking the geometrical and topological features of a WDN to its resilience to the failure of a pipe is investigated. Some innovative indices have been borrowed from fractal geometry and complex network theory to study WDNs. Considering all possible network configurations obtained by suppressing one link, the proposed indices are used to quantify the impact of pipe failure on the system's resilience. This approach aims to identify critical links, in terms of resilience, with the help of topological metrics only, and without recourse to hydraulic simulations, which require complex calibration processes and come with a computational burden. It is concluded that the proposed procedure, which has been successfully tested on two real WDNs located in southern Italy, can provide valuable information to water utilities about which pipes have a significant role in network performance, thus helping in their design, planning and management.

scholarly journals Research on the Structural Characteristics of Transmission Grid Based on Complex Network Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jinli Zhao ◽  
Hongshan Zhou ◽  
Bo Chen ◽  
Peng Li
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Statistical Characteristic ◽  
Network Theory ◽  
Structural Characteristics ◽  
Power Grid ◽  
Characteristic Parameters ◽  
Characteristic Structure ◽  
Complex Network Theory ◽  
Transmission Grid

Reasonable and strong structure is an important foundation for the smart transmission grid. For vigorously promoting construction of the smart grid, it is of great significance to have a thorough understanding of the complex structural characteristics of the power grid. The structural characteristics of several actual large-scale power grids of China are studied in this paper based on the complex network theory. Firstly, the topology-based network model of power grid is recalled for analyzing the statistical characteristic parameters. The result demonstrated that although some statistical characteristic parameters could reflect the topological characteristics of power grid from different ways, they have certain limitation in representing the electrical characteristics of power grid. Subsequently, the network model based on the electrical distance is established considering the limitation of topology-based model, which reflects that current and voltage distribution in the power grid are subject to Ohm's Law and Kirchhoff's Law. Comparing with the topology-based model, the electrical distance-based model performs better in reflecting the natural electrical characteristic structure of power grid, especially intuitive and effective in analyzing clustering characteristics and agglomeration characteristics of power grid. These two models could complement each other.

scholarly journals Measuring Software Modularity Based on Software Networks

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 344 ◽  
Author(s):  
Yiming Xiang ◽  
Weifeng Pan ◽  
Haibo Jiang ◽  
Yunfang Zhu ◽  
Hao Li
Keyword(s):  
Complex Systems ◽  
Complex Network ◽  
Software Metrics ◽  
Empirical Evaluation ◽  
Network Models ◽  
Software Systems ◽  
Complex Network Theory ◽  
Software Metric ◽  
Software Network ◽  
Software Modularity

Modularity has been regarded as one of the most important properties of a successful software design. It has significant impact on many external quality attributes such as reusability, maintainability, and understandability. Thus, proposing metrics to measure the software modularity can be very useful. Although several metrics have been proposed to characterize some modularity-related attributes, they fail to characterize software modularity as a whole. A complex network uses network models to abstract the internal structure of complex systems, providing a general way to analyze complex systems as a whole. In this paper, we introduce the complex network theory into software engineering and employ modularity, a metric widely used in the field of community detection in complex network research, to measure software modularity as a whole. First, a specific piece of software is represented by a software network, feature coupling network (FCN), where methods and attributes are nodes, couplings between methods and attributes are edges, and the weight on the edges denotes the coupling strength. Then, modularity is applied to the FCN to measure software modularity. We apply the Weyuker’s criteria which is widely used in the field of software metrics, to validate the modularity as a software metric theoretically, and also perform an empirical evaluation using open-source Java software systems to show its effectiveness as a software metric to measure software modularity.

scholarly journals Double Push Strategy of Knowledge for Product Design Based on Complex Network Theory

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Xue-rui Li ◽  
Sui-huai Yu ◽  
Jian-jie Chu ◽  
Deng-kai Chen ◽  
Lin-jian Wu
Keyword(s):  
Product Design ◽  
Complex Network ◽  
Network Model ◽  
Network Theory ◽  
Auxiliary Function ◽  
Triangular Fuzzy Number ◽  
Knowledge Network ◽  
Design Knowledge ◽  
Complex Network Theory ◽  
Scale Free

Reasonable application of design knowledge can help improve the efficiency and quality of product design. Based on complex network theory, this study proposes a double push strategy of knowledge for product design. The proposal introduces the concept of attribute similarity and triangular fuzzy number and uses the theory and method of complex network to build the knowledge network model for product design that contains creative knowledge subnetwork and engineering knowledge subnetwork. This paper is to understand the structure and dynamics of the knowledge network model and to identify and predict knowledge nodes and knowledge groups strongly related to design intent in view of the scale-free network topology analysis theory. We develop a double push strategy of product design knowledge to implement the effective auxiliary function for product design process. Finally, a design case of antalgic pump is presented to demonstrate the practicability and validity of the strategy.

Interplay of ICP and IXP over the Internet with power-law features

2018 ◽  
Vol 29 (01) ◽  
pp. 1850001
Author(s):  
Zhongyan Fan ◽  
Wallace Kit-Sang Tang
Keyword(s):  
Complex Network ◽  
Power Law ◽  
Homogeneous Model ◽  
Autonomous Systems ◽  
Network Models ◽  
Real Data ◽  
The Internet ◽  
Topological Features ◽  
Multi Level ◽  
Artificial Network

The Internet is the largest artificial network consisting of billions of IP devices, managed by tens of thousands of autonomous systems (ASes). Due to its importance, the Internet has received much attention and its topological features, mainly in AS-level, have been widely explored from the complex network perspective. However, most of the previous studies assume a homogeneous model in which nodes are indistinguishable in nature. It may be good for a general study of topological structure, but unfortunately it fails to reflect the functionality. The Internet ecology is in fact heterogeneous and highly complex. It consists of various elements such as Internet Exchange Points (IXPs), Internet Content Providers (ICPs), and normal Autonomous System (ASes), realizing different roles in the Internet. In this paper, we propose level-structured network models for investigating how ICP performs under the AS-topology with power-law features and how IXP enhances its performance from a complex network perspective. Based on real data, our results reveal that the power-law nature of the Internet facilitates content delivery not only in efficiency but also in path redundancy. Moreover, the proposed multi-level framework is able to clearly illustrate the significant benefits gained by ICP from IXP peerings.

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