SCRN: A Complex Network Reconstruction Method Based on Multiple Time Series

Abstract

Complex network reconfiguration has always been an important task in complex network research. Simple and effective complex network reconstruction methods can promote the understanding of the operation of complex systems in the real world. There are many complex systems, such as stock systems, social systems and thermal power systems. These systems generally produce correlated time series of data. Discovering the relationships among these multivariate time series is the focus of this research. This paper proposes a Spearman coefficient reconstruction network (SCRN) method based on the Spearman correlation coefficient. In the SCRN method, we select entities in the real world as the nodes of the network and determine connection weights of the network edges by calculating the Spearman correlation coefficients among nodes. In this paper, we selected a stock system and boiler equipment in a thermal power generation system to construct two complex network models. For the stock network model, we used the classic Girvan–Newman (GN) algorithm for community discovery to determine whether the proposed network topology is reasonable. For the boiler network model, we built a predictive model based on an support vector regression (SVR) model in machine learning, and we verified the rationality of the boiler model by predicting the amount of boiler steam.

Influencing Factors and Improvement Path of Synergistic Value Creation Efficiency of Emerging Enterprises

“…Meng proposes a Spearman coefficient reconstruction network (SCRN) method based on the Spearman correlation coefficient. In the SCRN method, we select entities in the real world as the nodes of the network and determine the connection weights of the network edges by calculating the Spearman correlation coefficients among nodes [ 12 ].…”

An analysis and prediction model based on complex network time series

“…At each independent time point, the complex network structure presents a specific connection relationship. From the time dimension, this complex network structure reflects the regular evolution characteristics (Berthelot et al, 2020; Meng, Jiang, & Wei, 2020). With the development of complex network research, scholars have put forward several methods to analyse the time series of complex networks to reveal the characteristics and evolution trend of a specific network structure (Ma et al, 2018; Savara, 2016).…”

“…On this basis, the development trend of complex network time series is predicted. Based on historical data, the prediction model of complex network time series is constructed to analyse and predict the changing trend of a complex network and extract its internal evolution mechanism (Anghinoni et al, 2019; Meng, Jiang, & Wei, 2020). Based on the overall measurement index of complex network time series, some scholars have proposed a variety of prediction algorithms, such as the causal complex network prediction method for multivariate time series (Jiang et al, 2017), the sliding window‐based algorithm for complex networks time series (Carmona et al, 2019), the complex network prediction from chaotic time series on Riemannian manifold (Sun, 2016), the prediction method of complex network evolution based on similar dynamics (Wu et al, 2020), the prediction of systemic risk contagion based on a dynamic complex network model using machine learning algorithm (Yu & Zhao, 2020), and the intelligent forecasting method of wave pattern based on multidimensional information complex network time series (Liu et al, 2018).…”