scholarly journals Corrigendum to “Cross-Shareholdings Structural Characteristic and Evolution Analysis Based on Complex Network”

2017 ◽  
Vol 2017 ◽  
pp. 1-1 ◽  
Author(s):  
Xiaohong Chang ◽  
Haiyun Wang
Keyword(s):  
Structural Characteristic ◽  
Complex Network ◽  
Evolution Analysis ◽  
Cross Shareholdings

Vehicle demand evolution analysis from the complex network perspective

2019 ◽  
Vol 532 ◽  
pp. 121889 ◽  
Author(s):  
Zhong-Yuan Jiang ◽  
Qiang Wang ◽  
Zhi-Quan Liu ◽  
Jian-Feng Ma
Keyword(s):  
Complex Network ◽  
Evolution Analysis ◽  
Vehicle Demand

Complexities’ day-to-day dynamic evolution analysis and prediction for a Didi taxi trip network based on complex network theory

2018 ◽  
Vol 32 (09) ◽  
pp. 1850062 ◽  
Author(s):  
Lin Zhang ◽  
Jian Lu ◽  
Jialin Zhou ◽  
Jinqing Zhu ◽  
Yunxuan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Complex Network ◽  
Bp Neural Network ◽  
Network Theory ◽  
Mobile App ◽  
Dynamic Evolution ◽  
Training Set ◽  
Management Level ◽  
Complex Network Theory ◽  
Evolution Analysis

Didi Dache is the most popular taxi order mobile app in China, which provides online taxi-hailing service. The obtained big database from this app could be used to analyze the complexities’ day-to-day dynamic evolution of Didi taxi trip network (DTTN) from the level of complex network dynamics. First, this paper proposes the data cleaning and modeling methods for expressing Nanjing’s DTTN as a complex network. Second, the three consecutive weeks’ data are cleaned to establish 21 DTTNs based on the proposed big data processing technology. Then, multiple topology measures that characterize the complexities’ day-to-day dynamic evolution of these networks are provided. Third, these measures of 21 DTTNs are calculated and subsequently explained with actual implications. They are used as a training set for modeling the BP neural network which is designed for predicting DTTN complexities evolution. Finally, the reliability of the designed BP neural network is verified by comparing with the actual data and the results obtained from ARIMA method simultaneously. Because network complexities are the basis for modeling cascading failures and conducting link prediction in complex system, this proposed research framework not only provides a novel perspective for analyzing DTTN from the level of system aggregated behavior, but can also be used to improve the DTTN management level.

Research on Internet Macroscopic Topology Structure

2013 ◽  
Vol 756-759 ◽  
pp. 1744-1747
Author(s):  
Shi Jie Guan ◽  
Hai Zhao
Keyword(s):  
Complex Network ◽  
Simulation Study ◽  
Research Method ◽  
Topology Structure ◽  
Evolution Analysis

This paper is aim to introduce the research background and the significance of the complex network on Internet macroscopic topology structure , then three subjects are putted forward that Generation method, simulation study and Evolution analysis are important on research on Internet macroscopic topology structure. At last,explain and analyse the research method on Internet macroscopic topology structure.

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.

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