Coarse graining method based on generalized degree in complex network

Long, Yong-Shang; Jia, Zhen; Wang, Yingying

doi:10.1016/j.physa.2018.03.080

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…In complex systems, the necessary data coarsening work can reduce data size and dimensionality without significantly compromising accuracy, thus improving analysis efficiency. Relevant methods have been widely applied in system science and computer science (Long et al, 2018;Zeng et al, 2019) Choosing a reasonable linkage symbol allows for a more effective analysis of the linkages between nickel futures price indices on different exchanges. As shown in Figure 1, the fluctuations in the nickel futures price indices on both exchanges are relatively concentrated and generally small, with most concentrated in the range of [-0.05,0.05], which suggests that a reasonable level of coarse-graining could simplify the data without filtering out too much information.…”

Section: Data Selection and Coarse-grained Processingmentioning

confidence: 99%

Analysis of Linkage Fluctuation in Time Series Data of Nickel Futures Price Index

Chen,

Huo,

Cao

2023

Journal of Business Economics and Management

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This paper explores the variation pattern of nickel futures prices using the daily closing levels of the nickel futures price index of the London Futures Exchange and the Shanghai Futures Exchange. The data coarse-graining method is employed to transform the continuous time series data of price index changes into symbols {P, N, M}, which are slid through continuous windows to form the modalities of price index linkage fluctuations. By treating the modalities as nodes and the transformations between them as edges, a weighted directed complex network is constructed to represent the linked volatility of the LME and SHFE nickel futures indices time series. The complex network is applied to analyse the network characteristics and obtain the inner pattern of the linked fluctuations. The results show that the complex network of time series linked volatility of the LME and SHFE nickel futures indices exhibits a power-law nature, with closely linked subgroups formed within it. And the mode transitions within these subgroups follow certain patterns. This paper also identifies core positioned modes and important intermediate modes that reflect the dynamics of nickel prices in reality. The method presented in this paper may be extended to related fields and has good applicability.

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Section: Data Selection and Coarse-grained Processingmentioning

confidence: 99%

Analysis of Linkage Fluctuation in Time Series Data of Nickel Futures Price Index

Chen,

Huo,

Cao

2023

Journal of Business Economics and Management

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“…Complex network science has interfused with many other scientific areas and has wider and wider real-world applications [1][2][3][4][5][6] . Plenty of real-world systems can be described or modeled by complex networks.…”

Section: Introductionmentioning

confidence: 99%

Statistical Identification of Important Nodes in Biological Systems

Wang

2021

J Syst Sci Complex

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Biological systems can be modeled and described by biological networks. Biological networks are typical complex networks with widely real-world applications. Many problems arising in biological systems can be boiled down to the identification of important nodes. For example, biomedical researchers frequently need to identify important genes that potentially leaded to disease phenotypes in animal and explore crucial genes that were responsible for stress responsiveness in plants. To facilitate the identification of important nodes in biological systems, one needs to know network structures or behavioral data of nodes (such as gene expression data). If network topology was known, various centrality measures can be developed to solve the problem; while if only behavioral data of nodes were given, some sophisticated statistical methods can be employed. This paper reviewed some of the recent works on statistical identification of important nodes in biological systems from three aspects, that is, 1) in general complex networks based on complex networks theory and epidemic dynamic models; 2) in biological networks based on network motifs; and 3) in plants based on RNA-seq data. The identification of important nodes in a complex system can be seen as a mapping from the system to the ranking score vector of nodes, such mapping is not necessarily with explicit form. The three aspects reflected three typical approaches on ranking nodes in biological systems and can be integrated into one general framework. This paper also proposed some challenges and future works on the related topics. The associated investigations have potential real-world applications in the control of biological systems, network medicine and new variety cultivation of crops.

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“…These methods can well maintain some of the original networks. In 2018, our research team proposed a coarse-graining method based on the generalized degree (GDCG) [19]. Specifically, the GDCG approach provides an adjustable generalized degree by parameter p for preserving a variety of significant properties of the initial networks during the coarse-graining processes.…”

Section: Introductionmentioning

confidence: 99%

Coarse Graining Method Based on Noded Similarity in Complex Network

Wang¹,

Jia²,

Zeng³

2018

Self Cite

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Coarse graining of complex networks is an important method to study large-scale complex networks, and is also in the focus of network science today. This paper tries to develop a new coarse-graining method for complex networks, which is based on the node similarity index. From the information structure of the network node similarity, the coarse-grained network is extracted by defining the local similarity and the global similarity index of nodes. A large number of simulation experiments show that the proposed method can effectively reduce the size of the network, while maintaining some statistical properties of the original network to some extent. Moreover, the proposed method has low computational complexity and allows people to freely choose the size of the reduced networks.

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Coarse graining method based on generalized degree in complex network

Cited by 7 publications

References 33 publications

Analysis of Linkage Fluctuation in Time Series Data of Nickel Futures Price Index

Analysis of Linkage Fluctuation in Time Series Data of Nickel Futures Price Index

Statistical Identification of Important Nodes in Biological Systems

Coarse Graining Method Based on Noded Similarity in Complex Network

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