جستجوی مقالات مرتبط با کلیدواژه "شبکه های پیچیده" در نشریات گروه "جغرافیا"

Network construction is an acceptable approach for better understanding the behavior of complex system which can be used to reveal the pattern of collective dynamics for realizing physical interactions in the dynamical system. In this case, characterizing functional connectivity of complex networks for studying a broad class of natural and artificial systems from the measures of correlation and causality is of utmost importance to correctly unravel physical phenomena of the system. Many network reconstruction approaches are based on heuristically thresholding the correlation matrices resulting from pairwise correlation analysis according to experimental methods. Other approaches compare the observed correlations against null models in the statistical analyses, obtaining results which are statistically robust. Different methods were used, including cross-correlation (CC), spectral coherence (SpeCoh), mutual information (MI), transfer entropy (TE), Spearman's rank correlation (SC) and convergent cross-mapping (CCM). The methods were applied to linear and nonlinear collective dynamics by autoregressive moving average (ARMA) and Logistic map (LOG) models, respectively. The dynamics of interconnected units was simulated from different complex topologies widely observed in empirical systems with well-known network models. The methods of MI and CCM were chosen after examining on the artificial cases consisting of desirable features of the real systems. The results show that network reconstruction dramatically depends on the interplay between the (unknown) underlying structure and the (unknown) underlying dynamics and high values of the area under curve of receiver operating characteristic around 72% obtain for the selected network topologies.

One of the small and young industries in the stock market is the tourism industry, which includes ‎several companies, ‎including the Cultural Heritage Investment Group. In the present ‎by using the complex networks theory two networks are ‎presented. First, a network is chosen from 36 ‎industries in Iran economy according to active industries in the stock ‎market using a fundamental approach. The weight of the edges is ‎determined according to a 36-section of input-output ‎table. In the next step, a correlation network ‎among the stock market indices of these 36 industries is formed using a ‎technical analysis approach. ‎ a centrality criteria is used to provide an approach to determine the position of the ‎tourism ‎industry as well as its connection with other industries in Iran stock market. According to the ‎results, the food industry has ‎the most impact on the ‎tourism due to the required food supplement for hotels and restaurants. The ‎industrial ‎contracting is the most affected by the tourism due to the impact of tourism ‎development on activities such as ‎the restoration of historic buildings. The stock market index of the tourism has the ‎highest correlation with the stock ‎market index of the Printing and‏ ‏publishing. ‎