scholarly journals Complex network characteristics of the planned subway station network in Hangzhou (2005-2022)

2020 ◽  
Vol 194 ◽  
pp. 05052
Author(s):  
Wu Fang ◽  
Zhu Yue ◽  
Zhu Wei ◽  
Hu Xiaojun ◽  
Shen Peng ◽  
...  
Keyword(s):  
Complex Network ◽  
Urban Areas ◽  
Small World ◽  
Clustering Coefficient ◽  
Network Density ◽  
Subway Station ◽  
Analysis Method ◽  
Network Characteristics ◽  
Scale Free ◽  
Density Clustering

The complex network characteristic of the planned subway station system is of great significance to the centrality layout of the existing urban areas. The three phases of the Hangzhou subway plan from 2005-2022 are selected as a case, and the complex network analysis method is used to study the centrality characteristic of the planned subway station system. The study found that with construction, the network density, clustering coefficient, average shortcut distance, all centrality indicators of the network gradually decreased; but in phase Ⅲadjustment, the average shortcut distance, three centrality potential indicators have all rebounded substantially. The newly-added airport rail express line has greatly increased the overall cohesion of the original subway network. The subway station networks of five planning stages all have smaller clustering coefficients and larger average shortcut distances, but none of them have a small-world characteristic, and the scale-free characteristic is still not obvious.

scholarly journals Complex network theory, streamflow, and hydrometric monitoring system design

2015 ◽  
Vol 19 (7) ◽  
pp. 3301-3318 ◽  
Author(s):  
M. J. Halverson ◽  
S. W. Fleming
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Small World ◽  
Detection Algorithm ◽  
Clustering Coefficient ◽  
Network Stability ◽  
Meteorological Forcing ◽  
Complex Network Theory ◽  
Scale Free ◽  
Seasonal Flow

Abstract. Network theory is applied to an array of streamflow gauges located in the Coast Mountains of British Columbia (BC) and Yukon, Canada. The goal of the analysis is to assess whether insights from this branch of mathematical graph theory can be meaningfully applied to hydrometric data, and, more specifically, whether it may help guide decisions concerning stream gauge placement so that the full complexity of the regional hydrology is efficiently captured. The streamflow data, when represented as a complex network, have a global clustering coefficient and average shortest path length consistent with small-world networks, which are a class of stable and efficient networks common in nature, but the observed degree distribution did not clearly indicate a scale-free network. Stability helps ensure that the network is robust to the loss of nodes; in the context of a streamflow network, stability is interpreted as insensitivity to station removal at random. Community structure is also evident in the streamflow network. A network theoretic community detection algorithm identified separate communities, each of which appears to be defined by the combination of its median seasonal flow regime (pluvial, nival, hybrid, or glacial, which in this region in turn mainly reflects basin elevation) and geographic proximity to other communities (reflecting shared or different daily meteorological forcing). Furthermore, betweenness analyses suggest a handful of key stations which serve as bridges between communities and might be highly valued. We propose that an idealized sampling network should sample high-betweenness stations, small-membership communities which are by definition rare or undersampled relative to other communities, and index stations having large numbers of intracommunity links, while retaining some degree of redundancy to maintain network robustness.

scholarly journals Exploring Railway Network Dynamics in China from 2008 to 2017

2018 ◽  
Vol 7 (8) ◽  
pp. 320 ◽  
Author(s):  
Yaping Huang ◽  
Shiwei Lu ◽  
Xiping Yang ◽  
Zhiyuan Zhao
Keyword(s):  
High Speed ◽  
Rapid Development ◽  
Small World ◽  
Clustering Coefficient ◽  
Network Clustering ◽  
Railway Network ◽  
High Speed Rail ◽  
Network Characteristics ◽  
Scale Free ◽  
Stage 1

China’s high speed rail (HSR) network has been rapidly constructed and developed during the past 10 years. However, few studies have reported the spatiotemporal changes of railway network structures and how those structures have been affected by the operation of high speed rail systems in different periods. This paper analyzes the evolving network characteristics of China’s railway network during each of the four main stages of HSR development over a 10-year period. These four stages include Stage 1, when no HSR was in place prior to August 2008; Stage 2, when several HSR lines were put into operation between August 2008, and July 2011; Stage 3, when the network skeleton of most main HSR lines was put into place. This covered the period until January 2013. Finally, Stage 4 covers the deep intensification of several new HSR lines and the rapid development of intercity-HSR railway lines between January 2013, and July 2017. This paper presents a detailed analysis of the timetable-based statistical properties of China’s railway network, as well as the spatiotemporal patterns of the more than 2700 stations that have been affected by the opening of HSR lines and the corresponding policy changes. Generally, we find that the distribution of both degrees and strengths are characterized by scale-free patterns. In addition, the decreasing average path length and increasing network clustering coefficient indicate that the small world characteristic is more significant in the evolution of China’s railway network. Correlations between different network indices are explored, in order to further investigate the dynamics of China’s railway system. Overall, our study offers a new approach for assessing the growth and evolution of a real railway network based on train timetables. Our study can also be referenced by policymakers looking to adjust HSR operations and plan future HSR routes.

scholarly journals Cross-Shareholdings Structural Characteristic and Evolution Analysis Based on Complex Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaohong Chang ◽  
Haiyun Wang
Keyword(s):  
Complex Network ◽  
Network Structure ◽  
Practical Importance ◽  
Small World ◽  
Clustering Coefficient ◽  
Policy Makers ◽  
Scale Free ◽  
Network Properties ◽  
The Cross ◽  
Cross Shareholdings

This study depicts the network morphology of firms which establish ties through cross-shareholdings by the theory of complex network analysis method. It calculates some complex network properties of the cross-shareholdings network and analyzes the evolution law of network structure in nearly 7 years. The network clearly displays small world properties and scale-free properties. The cross-shareholdings network average path length and clustering coefficient is with a small amplitude fluctuation; the network structure is relatively stable. Such a study is of practical importance and could provide opportunities for policy makers to improve the performance of the cross-shareholdings network.

scholarly journals Co-Movement in Stock Markets Based on Directed Complex Network

2016 ◽  
Vol 8 (2) ◽  
pp. 179
Author(s):  
Zhen Du ◽  
Pujiang Chen ◽  
Na Luo ◽  
Yingjie Tang
Keyword(s):  
Complex Network ◽  
Power Law ◽  
Degree Distribution ◽  
Path Length ◽  
Stock Exchange ◽  
Small World ◽  
Clustering Coefficient ◽  
Power Law Distribution ◽  
Scale Free ◽  
Set Up

<p>In this paper, directed complex network is applied to the study of A shares in SSE (Shanghai Stock Exchange). In order to discuss the intrinsic attributes and regularities in stock market, we set up a directed complex network, selecting 450 stocks as nodes between 2012 and 2014 and stock yield correlation connected as edges. By discussing out-degree and in-degree distribution, we find essential nodes in stock network, which represent the leading stock,. Moreover, we analyze directed average path length and clustering coefficient in the condition of different threshold, which shows that the network doesn’t have a small- world effect. Furthermore, we see that when threshold is between 0.08 and 0.15, the network follows the power-law distribution and behaves scale-free.</p>

SCALE-FREE AND SMALL-WORLD PROPERTIES OF A SPECIAL HIERARCHICAL NETWORK

Fractals ◽  
2019 ◽  
Vol 27 (02) ◽  
pp. 1950010
Author(s):  
DAOHUA WANG ◽  
YUMEI XUE ◽  
QIAN ZHANG ◽  
MIN NIU
Keyword(s):  
Degree Distribution ◽  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Hierarchical Network ◽  
Scale Free

Many real systems behave similarly with scale-free and small-world structures. In this paper, we generate a special hierarchical network and based on the particular construction of the graph, we aim to present a study on some properties, such as the clustering coefficient, average path length and degree distribution of it, which shows the scale-free and small-world effects of this network.

scholarly journals Quantitative method for resilience assessment framework of airport network during COVID-19

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260940
Author(s):  
Jiuxia Guo ◽  
Yang Li ◽  
Zongxin Yang ◽  
Xinping Zhu
Keyword(s):  
Complex Network ◽  
Small World ◽  
Global Scale ◽  
Assessment Framework ◽  
Small World Networks ◽  
Scale Free ◽  
Global Pandemic ◽  
Network Metrics ◽  
Resilience Assessment

The resilience and vulnerability of airport networks are significant challenges during the COVID-19 global pandemic. Previous studies considered node failure of networks under natural disasters and extreme weather. Herein, we propose a complex network methodology combined with data-driven to assess the resilience of airport networks toward global-scale disturbance using the Chinese airport network (CAN) and the European airport network (EAN) as a case study. The assessment framework includes vulnerability and resilience analyses from the network- and node-level perspectives. Subsequently, we apply the framework to analyze the airport networks in China and Europe. Specifically, real air traffic data for 232 airports in China and 82 airports in Europe are selected to form the CAN and EAN, respectively. The complex network analysis reveals that the CAN and the EAN are scale-free small-world networks, that are resilient to random attacks. However, the connectivity and vulnerability of the CAN are inferior to those of the EAN. In addition, we select the passenger throughput from the top-50 airports in China and Europe to perform a comparative analysis. By comparing the resilience evaluation of individual airports, we discovered that the factors of resilience assessment of an airport network for global disturbance considers the network metrics and the effect of government policy in actual operations. Additionally, this study also proves that a country’s emergency response-ability towards the COVID-19 has a significantly affectes the recovery of its airport network.

The Research of Complex Network on Archives Blog

2011 ◽  
Vol 181-182 ◽  
pp. 14-18
Author(s):  
Yi He
Keyword(s):  
Information Sharing ◽  
Complex Network ◽  
Network Model ◽  
Degree Distribution ◽  
Small World ◽  
Scale Free ◽  
Set Up

At the background of archives blog on Internet, this paper constructs a directed complex network model, and analyzes the network characters such as degree distribution. To verify its efficiency, we collect blogs’ information and set up a complex network..From the analysis result of the simulation and demonstration network, we know that they have the same characters, which show that, the virtual society network has small-world effect and scale-free character compared with real society network. The results indicate that the establishment of archives blog is favor to spread rapidly archives information, improve information sharing efficiency and promote the development of archives technology.

scholarly journals Studies on the Decrease Mechanisms of Typical Complex Networks

2021 ◽  
Author(s):  
Yuhu Qiu ◽  
Tianyang Lyu ◽  
Xizhe Zhang ◽  
Ruozhou Wang
Keyword(s):  
Average Length ◽  
Shortest Paths ◽  
Real Life ◽  
Network Models ◽  
Small World ◽  
Clustering Coefficient ◽  
Network Growth ◽  
Scale Free ◽  
Complex Network Models ◽  
High Degree

Network decrease caused by the removal of nodes is an important evolution process that is paralleled with network growth. However, many complex network models usually lacked a sound decrease mechanism. Thus, they failed to capture how to cope with decreases in real life. The paper proposed decrease mechanisms for three typical types of networks, including the ER networks, the WS small-world networks and the BA scale-free networks. The proposed mechanisms maintained their key features in continuous and independent decrease processes, such as the random connections of ER networks, the long-range connections based on nearest-coupled network of WS networks and the tendency connections and the scale-free feature of BA networks. Experimental results showed that these mechanisms also maintained other topology characteristics including the degree distribution, clustering coefficient, average length of shortest-paths and diameter during decreases. Our studies also showed that it was quite difficult to find an efficient decrease mechanism for BA networks to withstand the continuous attacks at the high-degree nodes, because of the unequal status of nodes.

scholarly journals Directed random geometric graphs

2019 ◽  
Vol 7 (5) ◽  
pp. 792-816
Author(s):  
Jesse Michel ◽  
Sushruth Reddy ◽  
Rikhav Shah ◽  
Sandeep Silwal ◽  
Ramis Movassagh
Keyword(s):  
Real World ◽  
Word Association ◽  
Graph Model ◽  
Small World ◽  
Geometric Graph ◽  
Clustering Coefficient ◽  
Random Geometric Graph ◽  
Random Geometric Graphs ◽  
Scale Free ◽  
Small World Property

Abstract Many real-world networks are intrinsically directed. Such networks include activation of genes, hyperlinks on the internet and the network of followers on Twitter among many others. The challenge, however, is to create a network model that has many of the properties of real-world networks such as power-law degree distributions and the small-world property. To meet these challenges, we introduce the Directed Random Geometric Graph (DRGG) model, which is an extension of the random geometric graph model. We prove that it is scale-free with respect to the indegree distribution, has binomial outdegree distribution, has a high clustering coefficient, has few edges and is likely small-world. These are some of the main features of aforementioned real-world networks. We also empirically observed that word association networks have many of the theoretical properties of the DRGG model.

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.

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