Measuring mixing patterns in complex networks by Spearman rank correlation coefficient

Zhang, Wenyao; Wei, Zong-Wen; Wang, Bing–Hong; Xiao, Han

doi:10.1016/j.physa.2016.01.056

Cited by 135 publications

(60 citation statements)

References 42 publications

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“…The Spearman coefficient is usually used to quantify how well two columns of data monotonically depend on each other, meaning the coefficient determines the degree to which each of the variables is correlated with the factor that is common to all variables in the analysis [63,64], i.e., the correlation study indicates the relationship between two monotonic variables. The resultant values will always be between +1 and −1.…”

Section: Discussionmentioning

confidence: 99%

Investment Uncertainty Analysis in Eucalyptus Bole Biomass Production in Brazil

Simões

Dinardi

Silva

2018

Forests

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Forestry investment projects for the biomass production of the eucalyptus bole can be characterized by uncertain environments, which result in economic risk to the forest producer, however that can be measured by applying probabilistic techniques. This was our motivation and justification for analyzing the economic-financial viability of different silvicultural practices for eucalyptus bole biomass production under conditions of uncertainties, running Monte Carlo method for risk management. The experiment was carried out in the state of São Paulo, Brazil, using 5 treatments with spacings of 3 m × 2 m; 3 m × 1 m; 1.5 m × 2 m; 3 m × 0.5 m; and 1.5 m × 1.0 m, i.e., different spacings between planting lines and plants. These treatments were characterized as investment projects. To develop stochastic models, we relied on technical-economic deterministic variables. We evaluated the investment projects based on the cash flows under conditions of uncertainty, discounted at the attractiveness rate calculated by capital asset pricing model. With the results of these economic flows, the net present value, the modified internal rate of return and the profitability index values were estimated, commonly used in the analysis of investments in projects. The results showed that based on economic metrics, the three-year rotation cycle for forest stands for biomass production of the main bole of eucalyptus, with a spacing of three meters between rows and two meters between plants, had an 83% probability of economic success. The sensitivity analysis showed that the bole biomass of eucalyptus is the most important variable for determining the economic-financial feasibility of the investment project.

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Section: Discussionmentioning

confidence: 99%

Investment Uncertainty Analysis in Eucalyptus Bole Biomass Production in Brazil

Simões

Dinardi

Silva

2018

Forests

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“…This method, however, makes it difficult to calculate analytically ρ even for a deterministic network such as the (u, v)-flower. An alternative way of ranking is to rank degrees of 2M end nodes in ascending order with assigning the average rank of degenerated degrees to them [27]. In this case, the rank of an end node with degree k is given by…”

Section: B Maximally Disassortative Networkmentioning

confidence: 99%

Fractality and degree correlations in scale-free networks

2017

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Fractal scale-free networks are empirically known to exhibit disassortative degree mixing. It is, however, not obvious whether a negative degree correlation between nearest neighbor nodes makes a scale-free network fractal. Here we examine the possibility that disassortativity in complex networks is the origin of fractality. To this end, maximally disassortative (MD) networks are prepared by rewiring edges while keeping the degree sequence of an initial uncorrelated scale-free network that is guaranteed to become fractal by rewiring edges. Our results show that most of MD networks with different topologies are not fractal, which demonstrates that disassortativity does not cause the fractal property of networks. In addition, we suggest that fractality of scale-free networks requires a long-range repulsive correlation in similar degrees.

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“…Unlike the Pearson correlation coefficient, Spearman's uses ranks instead of a single value in order to assess the monotonic relationship between two ranks. 40,41 SRCC was calculated for each participant for each question to check the variability in their answers compared…”

Section: Discussionmentioning

confidence: 99%

Colored apparel and its potential influence on heterosexual attraction

Sullivan

Kazlauciunas

Guthrie

2019

Color Research & Application

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The objective of this study is to determine if men would follow the "red effect" when choosing colors for women to wear on a date, and also to determine if the colors that men would wear when going on a date would be the same as the colors that females (their date) would wish them to wear. A set of psychophysical data was generated from this experiment, where participants were asked to rank a set of 10 colored samples based on preference for each question asked. There were three different sets of colored samples. The set of colored samples given to the participant depended on the question. A total of five questions were asked. Scaling analysis was done on the data to organize a set of items according to preferences providing values, an interval scale (Z values), that correspond to the relative perceptual differences among the stimuli. The Z values were graphed to show the general preference of colors for women to wear, and the preference of colors for men to wear. A Spearman's rank-order correlation coefficient (SRCC) was calculated comparing each individual's rank order with the mean rank order for that specific question. An average Spearman's rank order was calculated for each question and each gender in order to determine the variability in answers. Scaling results indicate that men follow the "red effect," but women preferred to wear other colors such as turquoise, blue, or yellow depending on the outfit. Males and females agreed that no matter the colored bottoms (denim or black), blue was the preferred color top for men to wear. SRCC results showed a lot of variability between individual answers and the mean answer indicating that participants' rankings did not necessarily agree with general color preferences presented in the scaling analysis. While scaling analysis might suggest certain color preferences such as men following the "red effect" and women preferring to wear blue, the poor correlation found using SRCC between the individual answers and the mean rank orders suggests that color preferences for each individual are inherently unique. K E Y W O R D Scolor apparel, color preference, color psychology, heterosexual attraction

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Measuring mixing patterns in complex networks by Spearman rank correlation coefficient

Cited by 135 publications

References 42 publications

Investment Uncertainty Analysis in Eucalyptus Bole Biomass Production in Brazil

Investment Uncertainty Analysis in Eucalyptus Bole Biomass Production in Brazil

Fractality and degree correlations in scale-free networks

Colored apparel and its potential influence on heterosexual attraction

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