Research on complex network system of growth pattern of scientific and technological innovation talents

2017 ◽  
Author(s):  
Zhang wen yu ◽  
Xue Yu ◽  
Ren Lu ◽  
He Zhen ◽  
Liu Chang
Keyword(s):  
Complex Network ◽  
Technological Innovation ◽  
Growth Pattern ◽  
Network System

Complex Network System Modeling and Optimization

2013 ◽  
Vol 303-306 ◽  
pp. 1948-1951
Author(s):  
Xian Tan
Keyword(s):  
Genetic Algorithm ◽  
Complex Network ◽  
Optimization Problem ◽  
System Modeling ◽  
Network System ◽  
Modeling And Optimization ◽  
Simple Genetic Algorithm

In order to solve the complicated network system modeling and optimization problem, in this paper, at first the simple genetic algorithm is analyzed and discussed in detail, and then the improvement of the genetic algorithm is studied, and finally summarizes the characteristics of the algorithm. Through the experiment, it is demonstrated that this method can effectively solve the complicated network of the two problems.

scholarly journals Neural Network Embedding of Functional Microconnectome.

2022 ◽  
Author(s):  
Arata Shirakami ◽  
Takeshi Hase ◽  
Yuki Yamaguchi ◽  
Masanori Shimono
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Complex Network ◽  
Second Step ◽  
Network System ◽  
Network Embedding ◽  
Neural Network System ◽  
Network Patterns ◽  
Network Metrics ◽  
Electrical Activities

Abstract Our brain works as a vast and complex network system. We need to compress the networks to extract simple principles of network patterns and interpret these paradigms to better comprehend their complexities. This study treats this simplification process using a two-step analysis of topological patterns of functional connectivities that were produced from electrical activities of ~1000 neurons from acute slices of mouse brains [Kajiwara et al. 2021] As the first step, we trained an artificial neural network system called neural network embedding (NNE) and automatically compressed the functional connectivities. As the second step, we widely compared the compressed features with 15 representative network metrics, having clear interpretations, including not only common metrics, such as centralities clusters and modules but also newly developed network metrics. The result demonstrates not only the fact that the newly developed network metrics could complementarily explain the features of what was compressed by the NNE method but was previously relatively hard to explain using common metrics such as hubs, clusters and communities. This NNE method surpasses the limitations of commonly used human-made metrics but also provides the possibility that recognizing our own limitations drives us to extend interpretable targets by developing new network metrics.

Crossed Synchronization of Multiple Subnets Complex Network System with Time-Varying Delay

2017 ◽  
Vol 19 (2) ◽  
pp. 606-613 ◽  
Author(s):  
Zhou Bi-feng ◽  
Lou Yi-ping ◽  
Zhong Yao-xiang
Keyword(s):  
Complex Network ◽  
Network System ◽  
Time Varying ◽  
Time Varying Delay ◽  
Varying Delay

scholarly journals Study of Cascading Failure in Multisubnet Composite Complex Networks

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 523
Author(s):  
Gengxin Sun ◽  
Chih-Cheng Chen ◽  
Sheng Bin
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Influencing Factors ◽  
Network Models ◽  
Network Robustness ◽  
Cascading Failure ◽  
Network System ◽  
Multiple Relationships ◽  
Hierarchical Network ◽  
Coupling Relationship

Current research on the cascading failure of coupling networks is mostly based on hierarchical network models and is limited to a single relationship. In reality, many relationships exist in a network system, and these relationships collectively affect the process and scale of the network cascading failure. In this paper, a composite network is constructed based on the multisubnet composite complex network model, and its cascading failure is proposed combined with multiple relationships. The effect of intranetwork relationships and coupling relationships on network robustness under different influencing factors is studied. It is shown that cascading failure in composite networks is different from coupling networks, and increasing the strength of the coupling relationship can significantly improve the robustness of the network.

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