scholarly journals An Evolutionary Algorithm of the Regional Collaborative Innovation Based on Complex Network

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Kun Wang ◽  
Duoyong Sun
Keyword(s):  
Evolutionary Algorithm ◽  
Complex Network ◽  
Innovation System ◽  
Network Evolution ◽  
National Bureau ◽  
Dynamic Features ◽  
Complex Network Theory ◽  
Collaborative Innovation ◽  
New Perspective ◽  
Supporting Condition

This paper proposed a new perspective to study the evolution of regional collaborative innovation based on complex network theory. The two main conceptions of evolution, “graph with dynamic features” and “network evolution,” have been provided in advance. Afterwards, we illustrate the overall architecture and capability model of the regional collaborative innovation system, which contains several elements and participants. Therefore, we can definitely assume that the regional collaborative innovation system could be regarded as a complex network model. In the proposed evolutionary algorithm, we consider that each node in the network could only connect to less than a certain amount of neighbors, and the extreme value is determined by its importance. Through the derivation, we have created a probability density function as the most important constraint and supporting condition of our simulation experiments. Then, a case study was performed to explore the network topology and validate the effectiveness of our algorithm. All the raw datasets were obtained from the official website of the National Bureau of Statistic of China and some other open sources. Finally, some meaningful recommendations were presented to policy makers, especially based on the experimental results and some common conclusions of complex networks.

A study on the evolution of port logistics network based on complex theory: A case study of key coastal ports in China

2021 ◽  
pp. 1-11
Author(s):  
Xu Xu ◽  
Yanbing Yang ◽  
Yi Liu
Keyword(s):  
Complex Network ◽  
Network Evolution ◽  
Clustering Coefficient ◽  
Belt And Road Initiative ◽  
Power Law Distribution ◽  
Complex Network Theory ◽  
Network Characteristics ◽  
Real Network ◽  
Belt And Road ◽  
The Belt And Road

In order to promote the construction of “The Belt and Road” Initiative, we construct the complex network evolution model based on the complex network theory and node attractiveness. We select the relevant 2017 data of 18 coastal ports mentioned in the Belt and Road Initiative, and verify the validity of the model by comparing the network eigenvalues between evolving network and real network. The results show that the average distances between ports are short, clustering coefficient and dependence of hub ports are high, its topological structure has scale-free network characteristics and fits the power law distribution. Meanwhile, we study the change of network characteristics of the evolutionary network and real network under deliberate attack and random attack. The statistics show that the robustness is weak under deliberate attack but strong under random attack. These are great reference to the construction and development of China’s Belt and Road Initiative.

scholarly journals A Novel Algorithm of Identification Theory of Complex Network for Public Transportation Network Cascading Failure

2021 ◽  
Vol 15 ◽  
pp. 236-247
Author(s):  
Xu Xu
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Public Transportation ◽  
Transportation Network ◽  
Network Evolution ◽  
Cascading Failure ◽  
Complex Network Theory ◽  
Node Importance ◽  
Public Transportation Network ◽  
Bus Network

With the development of complex network theory and the gradual application of the traffic field, the problem of cascading failure has caused great attention of researchers. This paper tries to propose a new method based on complex network theory to measure the importance of nodes in the network. Based on complex network theory, this paper first discusses the network evolution mechanism of three main contents, define the importance of nodes in the network, and the design of the network center and the evaluation of the importance of node algorithm. In the end, a critical section identification method considering the failure probability and the failure consequence is designed, and the method for calculating the node importance based on the cascading failure is proposed. Using complex network theory, a quantitative assessment of the center of public transportation network and node importance model is designed. The bus network center, for the study of node importance analysis of bus network survivability has important significance. Help guide the optimization of public transport network service. Improve transport capacity of public transportation system.

Reliability analysis of Tianjin urban rail transit network based on complex network evolution characteristics

2020 ◽  
pp. 2150075
Author(s):  
Junwei Zeng ◽  
Yongsheng Qian ◽  
Xiaodi Liu ◽  
Chaoyang Zhang ◽  
Dejie Xu ◽  
...  
Keyword(s):  
Complex Network ◽  
Rapid Development ◽  
Network Evolution ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Complex Network Theory ◽  
Urban Rail ◽  
Evolution Characteristics ◽  
Urban Rail Transit Network

The rapid development of new urbanization boosts the pace of urban rail transit (URT) network construction. The new-built lines not only facilitate people’s daily travel, but also change the reliability of the original network. In this paper, based on the complex network theory, the static characteristics of Tianjin URT represented by the current and forward networks are analyzed. The global efficiency is selected as the evaluation index to analyze the evolution characteristics of the network. The results show that Zhigu station and Xiawafang station are more vulnerable in the current network, while Zhangxingzhuang station and Jinzhonghe street station are the key stations in the forward network, and need to be paid more attention. Meanwhile, the lines M9 and Z2 as the key lines in the long-term network and their reliability must be ensured.

Analysing evolutionary algorithm dynamics using complex network theory: a primary study

2013 ◽  
Vol 4 (4) ◽  
pp. 339
Author(s):  
Weifeng Pan ◽  
Jing Wang ◽  
Chengxiang Yuan ◽  
Jianming Zhang ◽  
Hongyan Xue
Keyword(s):  
Evolutionary Algorithm ◽  
Complex Network ◽  
Network Theory ◽  
Primary Study ◽  
Complex Network Theory

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.

scholarly journals Dynamic Evolution Model of a Collaborative Innovation Network from the Resource Perspective and an Application Considering Different Government Behaviors

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 138 ◽  
Author(s):  
Wu ◽  
Shao ◽  
Feng
Keyword(s):  
Network Evolution ◽  
Evolution Model ◽  
Dynamic Evolution ◽  
Innovation Network ◽  
Transfer Policy ◽  
Collaborative Innovation ◽  
Innovation Output ◽  
The Government ◽  
Small Change ◽  
Innovative Behaviors

The evolution of a collaborative innovation network depends on the interrelationships among the innovation subjects. Every single small change affects the network topology, which leads to different evolution results. A logical relationship exists between network evolution and innovative behaviors. An accurate understanding of the characteristics of the network structure can help the innovative subjects to adopt appropriate innovative behaviors. This paper summarizes the three characteristics of collaborative innovation networks, knowledge transfer, policy environment, and periodic cooperation, and it establishes a dynamic evolution model for a resource-priority connection mechanism based on innovation resource theory. The network subjects are not randomly testing all of the potential partners, but have a strong tendency to, which is, innovation resource. The evolution process of a collaborative innovation network is simulated with three different government behaviors as experimental objects. The evolution results show that the government should adopt the policy of supporting the enterprises that recently entered the network, which can maintain the innovation vitality of the network and benefit the innovation output. The results of this study also provide a reference for decision-making by the government and enterprises.

scholarly journals Research on the Uncertainty Decision Model of the Regional Collaborative Innovation System Based on an Improved Ant Colony Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Xiaona Zhang ◽  
Fayin Wang
Keyword(s):  
Decision Model ◽  
Ant Colony Algorithm ◽  
Innovation System ◽  
Path Selection ◽  
Collaborative Optimization ◽  
Ant Colony ◽  
Evaluation Index System ◽  
Regional Innovation ◽  
Collaborative Innovation ◽  
Improved Ant Colony Algorithm

The regional collaborative innovation system is a nonlinear complex system, which has obvious uncertainty characteristics in the aspects of member selection and evolution. Ant colony algorithm, which can do the uncertainty collaborative optimization decision-making, is an effective tool to solve the uncertainty decision path selection problem. It can improve the cooperation efficiency of each subsystem and achieve the goal of effective cooperation. By analysing the collaborative evolution mechanisms of the regional innovation system, an evaluation index system for the regional collaborative innovation system is established considering the uncertainty of collaborative systems. The collaborative uncertainty decision model is constructed to determine the regional innovation system’s collaborative innovation effectiveness. The improved ant colony algorithm with the pheromone evaporation model is applied to traversal optimization to identify the optimal solution of the regional collaborative innovation system. The collaboration capabilities of the ant colony include pheromone diffusion so that local updates are more flexible and the result is more rational. Finally, the method is applied to the regional collaborative innovation system.

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