The Study on the Relationship between Spatial Network Structure of Teaching Architecture and Students' Flow Behavior Based on Complex Network Analysis

2018 ◽  
Vol 878 ◽  
pp. 183-196
Author(s):  
Ke Xie ◽  
Fang Wu ◽  
Jia Li Wu ◽  
Sheng Li ◽  
Jia Hui Wang ◽  
...  

Informal learning is an important part of active learning in higher education. It is of great significance to create a good informal learning space for higher education. However, the current design of university buildings is lack of quality informal learning space. This paper analyzes the relationship between complex network analysis and student behavior, and finds that there exists a close relationship between them. The spatial structure has the essential impact on the distribution of the informal learning. The construction of streamline system places an important role in the formation of learning space network. The density of the network relationship is not a key factor, but the relationship model presents more important. The cohesion of network plays an important role in the formation of spatial network of learning. In the network structure, since the groups with cohesion power are capable to convey the information even faster, the regeneration inside the groups can be achieved through the flow of resource/information.

2021 ◽  
Vol 2 (1) ◽  
pp. 113-139
Author(s):  
Dimitrios Tsiotas ◽  
Thomas Krabokoukis ◽  
Serafeim Polyzos

Within the context that tourism-seasonality is a composite phenomenon described by temporal, geographical, and socio-economic aspects, this article develops a multilevel method for studying time patterns of tourism-seasonality in conjunction with its spatial dimension and socio-economic dimension. The study aims to classify the temporal patterns of seasonality into regional groups and to configure distinguishable seasonal profiles facilitating tourism policy and development. The study applies a multilevel pattern recognition approach incorporating time-series assessment, correlation, and complex network analysis based on community detection with the use of the modularity optimization algorithm, on data of overnight-stays recorded for the time-period 1998–2018. The analysis reveals four groups of seasonality, which are described by distinct seasonal, geographical, and socio-economic profiles. Overall, the analysis supports multidisciplinary and synthetic research in the modeling of tourism research and promotes complex network analysis in the study of socio-economic systems, by providing insights into the physical conceptualization that the community detection based on the modularity optimization algorithm can enjoy to the real-world applications.


2020 ◽  
Vol 67 (6) ◽  
pp. 1134-1138 ◽  
Author(s):  
Zhongke Gao ◽  
Hongtao Wang ◽  
Weidong Dang ◽  
Yongqiang Li ◽  
Xiaolin Hong ◽  
...  

Author(s):  
Emerson Luiz Chiesse da Silva ◽  
Marcelo De Oliveira Rosa ◽  
Keiko Veronica Ono Fonseca ◽  
Ricardo Luders ◽  
Nadia Puchaslki Kozievitch

2018 ◽  
Vol 55 ◽  
pp. 133-142 ◽  
Author(s):  
Wenyu Hou ◽  
Huifang Liu ◽  
Hui Wang ◽  
Fengyang Wu

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