scholarly journals Complex Network Analysis of Transmission Mechanism for Sustainable Incentive Policies

2020 ◽  
Vol 12 (2) ◽  
pp. 745 ◽  
Author(s):  
Lingyan Li ◽  
Lujiao Feng ◽  
Xiaotong Guo ◽  
Haiyan Xie ◽  
Wei Shi
Keyword(s):  
Complex Network ◽  
Rational Expectations ◽  
Clustering Coefficient ◽  
Transmission Mechanism ◽  
Sustainable Construction ◽  
Eastern Coast ◽  
Relationship Matrix ◽  
Transmission Path ◽  
External Impact ◽  
Incentive Policies

Existing research mainly focuses on the external impact of incentive policies of industrialized/manufactured construction (IMC). However, it is still unclear how the transmission mechanism among cities and regions of IMC incentive policies works in the process of formulation. To fill the knowledge gap, this study establishes a relationship matrix to propose the transmission-weighted complex network (TWCN). Degree distribution and clustering coefficient are used to calculate the transmission path and the transmission intensity of TWCN. The validation is based on data collected from 415 policy documents (2010–2018) and 2923 items from 181 nodes of the TWCN for IMC policies. The findings show that transmission path of IMC incentive policies is from the eastern coast of China to the central, western and northern regions. Fiscal and taxation incentives have the greatest intensity of spatial agglomeration in the transmission process. The results of the TWCN are consistent and conform to the scientific and rational expectations of research. Overall, the research outcomes are applicable to studies on sustainability policies in different fields, including sustainable construction, renewable energy, etc. Policy makers can implement the TWCN to recognize the performance and functions of different incentives and propose effective strategies to achieve sustainability.

A Discovery Method of the Dirty Data Transmission Path Based on Complex Network

2014 ◽  
Vol 651-653 ◽  
pp. 1741-1747
Author(s):  
Xiao Lin Zhao ◽  
Gang Hao ◽  
Chang Zhen Hu ◽  
Zhi Qiang Li
Keyword(s):  
Complex Network ◽  
Data Transmission ◽  
Software System ◽  
Power Law Distribution ◽  
Scale Free ◽  
Transmission Path ◽  
Binary File ◽  
Weighted Directed Graph ◽  
Dirty Data ◽  
Discovery Method

With the increasing scale of software system, the interaction between software elements becomes more and more complex, which lead to the increased dirty data in running software system. This may reduce the system performance and cause system collapse. In this paper, we proposed a discovery method of the dirty data transmission path based on complex network. Firstly, the binary file is decompiled and the function call graph is drawn by using the source code. Then the software structure is described as a weighted directed graph based on the knowledge of complex network. In addition, the dirty data node is marked by using the power-law distribution characteristics of the scale-free network construction of complex network chart. Finally, we found the dirty data transmission path during software running process. The experimental results show the transmission path of dirty data is accurate, which confirmed the feasibility of the method.

scholarly journals Impact of Urban Rail Transit Network on Residential and Commercial Land Values in China: A Complex Network Perspective

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shiping Wen ◽  
Jiangang Shi ◽  
Wei Zhang
Keyword(s):  
Complex Network ◽  
Urban Rail Transit ◽  
Clustering Coefficient ◽  
Rail Transit ◽  
Value Capture ◽  
Effective Measure ◽  
Transit Network ◽  
Land Prices ◽  
Urban Rail ◽  
Urban Rail Transit Network

Urban rail transit can improve a city’s accessibility. However, high construction and operation costs restrict the development of urban rail transit. Value capture recoups the additional value that the investments of urban rail transit confer to local land and is considered to be an effective measure to alleviate this financial problem. Understanding the land value uplift effects of urban rail transit is essential for understanding value capture. This study applied a Space-P model of urban rail transit network based on complex network theory and demonstrated the influence of urban rail transit network characteristics on residential and commercial land prices. The model was tested with eight metropolises in China, using the 2003 to 2022 timeframe as the context. The results showed a significant positive correlation between the number of nodes and the land prices, the average clustering coefficient was highly positively correlated with the land prices, and there was a significant negative correlation between the average path length and the land prices. This study provides theoretical support for value capture, is beneficial for urban rail transit planning, and supports improvements in the development quality of urban rail transit networks.

scholarly journals A Complex Network Approach for Quantitative Characterization and Robustness Analysis of Sandstone Pore Network Structure

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yuhao Hu ◽  
Guannan Liu ◽  
Feng Gao ◽  
Fengtian Yue ◽  
Tao Gao
Keyword(s):  
Pore Structure ◽  
Complex Network ◽  
Degree Distribution ◽  
Path Length ◽  
Robustness Analysis ◽  
Pore Network ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Random Disturbance ◽  
Pagerank Algorithm

The rational characterization and quantitative analysis of the complex internal pore structure of rock is the foundation to solve many underground engineering problems. In this paper, CT imaging technology is used to directly characterize the three-dimensional pore network topology of sandstone with different porosity. Then, in view of the problem, which is difficult to quantify the detailed topological structure of the sandstone pore networks in the previous study, the new complex network theory is used to characterize the pore structure. PageRank algorithm is based on the number of connections between targets as a measure index to rank the targets, so the network degree distribution, average path length, clustering coefficient, and robustness based on PageRank algorithm and permeability-related topological parameters are studied. The research shows that the degree distribution of sandstone pore network satisfies power law distribution, and it can be characterized by scale-free network model. The permeability of rock is inversely proportional to the average path length of sandstone network. The sandstone pore network has strong robustness to random disturbance, while a small number of pores with special topological properties play a key role in the macroscopic permeability of sandstone. This study attempts to provide a new perspective of quantifying the microstructure of the pore network of sandstone and revealing the microscopic structure mechanism of macroscopic permeability of pore rocks.

Monetary Transmission in the Term Structure of Interest Rates in Spain (1995-2003)

2021 ◽  
Vol 26 (1) ◽  
pp. 279-292
Author(s):  
María A. Prats ◽  
Gloria M. Soto
Keyword(s):  
Interest Rates ◽  
Term Structure ◽  
Rational Expectations ◽  
Transmission Mechanism ◽  
Monetary Transmission ◽  
Var Model ◽  
Expectations Hypothesis ◽  
Weak Evidence ◽  
One Year ◽  
The One

The aim of this paper is to investigate whether the effectiveness of the transmission mechanism of monetary  policy in Spain has changed since EMU establishment. The analysis is based on the fulfillment of the Expectations Hypothesis under rational expectations and the methodology is implemented through a  cointegrated  bivariate VAR model. The results reveal the existence of  monetary transmission in the term structure in the  period prior to EMU, even though the evidence is stronger up to the one-year rate. From 1999, the results are   only consistent with a weak evidence of monetary transmission.

scholarly journals Application of Complex Network Principles to Key Station Identification in Railway Network Efficiency Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Li Wang ◽  
Min An ◽  
Limin Jia ◽  
Yong Qin
Keyword(s):  
Complex Network ◽  
Policy Development ◽  
Network Models ◽  
Efficiency Analysis ◽  
Physical Structure ◽  
Influential Factors ◽  
Clustering Coefficient ◽  
Network Efficiency ◽  
Railway Network ◽  
Rail Network

Network efficiency analysis becomes important in railways in order to contribute towards improving the safety and capacity of the rail network, making rail travel more attractive for passengers, and improving industry practice and informing policy development. However, a physical railway network structure is a complicated system, and the operation, maintenance, and management of such a network is a difficult task which may be affected by many influential factors. By using efficiency analysis technology for a railway network, combining physical structure with operation functions can help railway industry to optimize the railway network while improving its efficiency and reliability. This paper presents a new methodology based on complex network principles that combines the physical railway structure with railway operation strategy for a railway network efficiency analysis. In this method, two network models of railway physical and train flow networks are developed for the identification of key stations in the railway network based on network efficiency contribution in which the terms of degree, strength, betweenness, clustering coefficient, and a comprehensive factor are taken into consideration. Once the key stations have been identified and analysed, the railway network efficiency is then studied on the basis of selective and random modes of the station failures. A case study is presented in this paper to demonstrate the application of the proposed methodology. The results show that the identified key stations in the railway network play an important role in improving the overall railway network efficiency, which can provide useful information to railway designers, engineers, operators and maintainers to operate and maintain railway network effectively and efficiently.

scholarly journals Classification of Literary Works: Fractality and Complexity of the Narrative, Essay, and Research Article

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 904
Author(s):  
Aldo Ramirez-Arellano
Keyword(s):  
Complex Network ◽  
Path Length ◽  
Research Topic ◽  
Clustering Coefficient ◽  
Node Degree ◽  
Original Text ◽  
Literary Works ◽  
Research Article ◽  
A Current

A complex network as an abstraction of a language system has attracted much attention during the last decade. Linguistic typological research using quantitative measures is a current research topic based on the complex network approach. This research aims at showing the node degree, betweenness, shortest path length, clustering coefficient, and nearest neighbourhoods’ degree, as well as more complex measures such as: the fractal dimension, the complexity of a given network, the Area Under Box-covering, and the Area Under the Robustness Curve. The literary works of Mexican writers were classify according to their genre. Precisely 87% of the full word co-occurrence networks were classified as a fractal. Also, empirical evidence is presented that supports the conjecture that lemmatisation of the original text is a renormalisation process of the networks that preserve their fractal property and reveal stylistic attributes by genre.

Analysis of Migraine Induced Monitoring Imaging Data by Multilayer Mixed Cluster Detection

2019 ◽  
Vol 9 (6) ◽  
pp. 1278-1283
Author(s):  
Min Shi ◽  
Dong Dong Yang ◽  
Yuan Zhou ◽  
Huan Zhao ◽  
Yu Fang
Keyword(s):  
Complex Network ◽  
Visual Processing ◽  
Brain Function ◽  
Clinical Symptoms ◽  
Clustering Coefficient ◽  
Hill Climbing ◽  
Ensemble Clustering ◽  
Imaging Data ◽  
Characteristic Path Length ◽  
The Difference

The complex network of resting brain function was constructed by graph theory to study the difference of network topology between migraine patients and normal people. The complex network of brain function of the two groups was constructed respectively, and the average clustering coefficient, characteristic path length, small cosmopolitan, homology, median centrality and other measurement parameters of the two groups of complex networks were calculated and compared. The multi-layer hybrid ensemble clustering detection is introduced for data analysis, and the edge connectivity of consensus is optimized by modular analysis combined with hill climbing algorithm to improve the performance of the multi-layer hybrid ensemble clustering detection process driven by modularity. Conclusion: The abnormal areas of resting brain function network in migraine patients are related to pain management, visual processing and sensory relay, the findings of this study are helpful to better explain the clinical symptoms of migraine.

Research on the Structure of Peer-to-Peer Overlay Based on the Complex Network Theory

2012 ◽  
Vol 263-266 ◽  
pp. 1096-1099
Author(s):  
Zhi Yong Jiang
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Degree Distribution ◽  
Path Length ◽  
Small World ◽  
Peer To Peer ◽  
Clustering Coefficient ◽  
Power Law Distribution ◽  
Complex Network Theory ◽  
The Mean

Relationship between nodes in peer-to-peer overlay, currently becomes a hot topic in the field of complex network. In this paper a model of peer-to-peer overlay was purposed. And then the paper focused on figuring out the mean-shortest path length (MSPL), clustering coefficient (CC) and the degree of every node which allowed us to discover the degree distribution. The results show that the degree distribution function follows approximately power law distribution and the network possesses notable clustering and small-world properties.

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.

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