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

Author(s):  
Zhang wen yu ◽  
Xue Yu ◽  
Ren Lu ◽  
He Zhen ◽  
Liu Chang
2013 ◽  
Vol 303-306 ◽  
pp. 1948-1951
Author(s):  
Xian Tan

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.


2022 ◽  
Author(s):  
Arata Shirakami ◽  
Takeshi Hase ◽  
Yuki Yamaguchi ◽  
Masanori Shimono

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.


2017 ◽  
Vol 19 (2) ◽  
pp. 606-613 ◽  
Author(s):  
Zhou Bi-feng ◽  
Lou Yi-ping ◽  
Zhong Yao-xiang

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 523
Author(s):  
Gengxin Sun ◽  
Chih-Cheng Chen ◽  
Sheng Bin

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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