The Complex Network Analysis of Power Grid: A Case Study of the West Bengal Power Network

Author(s):  
Himansu Das ◽  
Gouri Sankar Panda ◽  
Bhagaban Muduli ◽  
Pradeep Kumar Rath
2018 ◽  
Vol 14 (10) ◽  
Author(s):  
Gabriela Da Cruz Martins ◽  
Fabiano Ribeiro ◽  
Leonardo Santos Oliveira ◽  
Fabricio Luchesi Forgerini

2020 ◽  
Vol 12 (2) ◽  
pp. 538 ◽  
Author(s):  
Shaopei Chen ◽  
Dachang Zhuang

This paper takes the metro network of Guangzhou as a case study, and provides a quantitative analysis of the historical development of the network from 1999 to 2018. Particularly, the evolution of the topological structure of the Guangzhou Metro Network (GMN) is evaluated and characterized through the integration of geographic information system (GIS) and complex network analysis. The results show that: (1) The metro network of Guangzhou possesses the basic characteristics of small-world network, (2) with the development of GMN, the network complexity is increased and the spatial dispersion of the nodes tends to ease, but the average travel time and transfer rate continues to rise up, leading to the decreasing of the network transmission efficiency and the scattering of the nodes, (3) a good fault tolerance of the overall metro network of Guangzhou is revealed, but the spatial variance is observed, (4) the peak of degree centrality (DC) of the nodes is gradually moving northward along “Kecun Station–Guangzhou railway station–Jiahe Wanggang station”, while the peak of betweenness centrality (BC) is changing from “Kecun station” to “Jiahe Wanggang station”, and Jiahe Wanggang station has evolved into the most critical node in the current metro network of Guangzhou. In conclusion, this study should provide the scientific basis and significant decision-making support to the planning and operation management of GMN.


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 ◽  
...  

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