Structure analysis and core community detection of embodied resources networks among regional industries

He, Xijun; Dong, Yan-bo; Yuying, Wu; Wei, Guodan; Xing, Lizhi; Jia, Yan

doi:10.1016/j.physa.2017.02.068

Cited by 19 publications

(7 citation statements)

References 21 publications

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“…A recently published survey paper [26] has reviewed a large number of community detection algorithms in multidisciplinary applications considering both disjoint and overlapping community detection problems. These applications include the study of social networks [25,27], communication networks [28,29], engineering systems and networks [18,30], biology and ecology [31,32], health sciences [33], scientometrics [34,35], economics [36], etc.…”

Section: Community Detection: General Overviewmentioning

confidence: 99%

“…Most real networks (graphs) representing real systems have clusters, such that many edges connect nodes within the same cluster, and comparatively few edges connect nodes in different clusters. This is why community detection [13][14][15] has gained popularity in recent years, especially among researchers working with complex networks [16][17][18]. In particular, community detection has been applied in field of electrical engineering, including the management of power grids [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids

et al. 2019

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Symmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively).

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Section: Community Detection: General Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids

et al. 2019

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“…The most important recent issue has been the dynamics of collaborative innovation among industries [30,31]. Moreover, a lot of countries are promoting industrial cluster policies that facilitate collaborative innovation among industries in specific regions, and emphasizing that the key is creating networks among industries [32][33][34][35]. Patent data is an open and available data source.…”

Section: Patent Collaboration Networkmentioning

confidence: 99%

Exploring and Visualizing the Patent Collaboration Network: A Case Study of Smart Grid Field in China

et al. 2019

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Smart grids (SGs) have been widely recognized as an enabling technology for delivering sustainable energy transitions. SGs have a positive effect on the development of the world economy and society. SG construction plays an important role in responding to global climate change and promoting the sustainable development of the world economy and society. Under such a background, this paper attempts to investigate patent collaborations of the SG field in China. Based on the application data of collaborative patents from State Intellectual Property Office (SIPO) in China, this study employs complex network theory and social network analysis (SNA) method and conducts in-depth research on the patent collaboration network of SG field in China. The trend of patent collaboration was examined, the collaboration network of SG-related patents was investigated, the network characteristics, and the network structure were also explored. The results show that the proportion of enterprises participating in patent collaboration is relatively large for SG field in China, the percentage of collaboration relationships formed by different patent applicants varies greatly, and that the State Grid Corporation of China (SGCC) plays an important role in patent collaboration in SG field currently. It can also be found that patent collaboration is an effective form of cooperative innovation among different entities. Finally, three suggestions are provided in response to the existing problems.

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“…In essence, the intermediate matrix of the input–output table is a complex network in the sense that industries as nodes are linked with the exchange of products between industries whose structure can be characterized by power–law distributions or similar ones [ 2 , 3 , 4 , 5 , 6 ]. The complex network theory is employed by a large number of scholars to detect key industries [ 7 , 8 ] and industrial communities [ 9 , 10 ] and probe the risks of the transmission mechanism [ 11 , 12 ] in the input–output system.…”

Section: Introductionmentioning

confidence: 99%

Research on the Robustness of the Chinese Input–Output Network Based on Relative Entropy Theory

Wang

Xing

2022

Entropy

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The input–output (IO) network is the quantitative description of an IO-based economy in which nodes represent industries and edges connecting nodes represent the economic connection between industries. Robustness refers to the ability of tolerating perturbations that might affect the system’s functional body. There is both practical and theoretical significance to explore the robustness of the IO network for economic development. In this paper, we probe the robustness of the Chinese IO network based on the relative entropy of the probability distribution of network parameters (node degree, strongest path betweenness, downstream closeness and upstream closeness) under random node or edge failure and intentional node or edge attack. It is found that the Chinese IO network shows relatively weak robustness when it is under intentional attack, but relatively strong robustness when it is under random failure. Our experiment also verifies the applicability and effectiveness of the relative entropy model in measuring the robustness of the IO network.

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Structure analysis and core community detection of embodied resources networks among regional industries

Cited by 19 publications

References 21 publications

Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids

Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids

Exploring and Visualizing the Patent Collaboration Network: A Case Study of Smart Grid Field in China

Research on the Robustness of the Chinese Input–Output Network Based on Relative Entropy Theory

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