Power-law partial correlation network models

Although online political incivility has increasingly become an object of scholarly inquiry, there exists little agreement on the construct’s precise definition. The goal of this work was therefore to explore the relational dynamics among previously identified dimensions of online political incivility. The results of a regularized partial correlation network indicated that a communicator’s desire to exclude attitude-discrepant others from discussion played an especially influential role in the variable network. The data also suggested that certain facets of incivility may be likely to be deployed together. Specifically, the data suggested the existence of two identifiable groupings of incivility factors: (1) variables pertaining to violation of speech-based norms and (2) variables pertaining to the violation of the inclusion-based norms that underlie democratic communication processes. These results are discussed in the context of political discussion and deliberation.

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The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis

NeuroImage ◽

10.1016/j.neuroimage.2008.05.059 ◽

2008 ◽

Vol 42 (3) ◽

pp. 1178-1184 ◽

Cited By ~ 734

Author(s):

Peter Fransson ◽

Guillaume Marrelec

Keyword(s):

Network Analysis ◽

Default Mode Network ◽

Partial Correlation ◽

Cingulate Cortex ◽

Posterior Cingulate Cortex ◽

Pivotal Role ◽

Correlation Network ◽

Default Mode ◽

Posterior Cingulate

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Truncation of Power Law Behavior in “Scale-Free” Network Models due to Information Filtering

Physical Review Letters ◽

10.1103/physrevlett.88.138701 ◽

2002 ◽

Vol 88 (13) ◽

Cited By ~ 138

Author(s):

Stefano Mossa ◽

Marc Barthélémy ◽

H. Eugene Stanley ◽

Luís A. Nunes Amaral

Keyword(s):

Power Law ◽

Information Filtering ◽

Network Models ◽

Scale Free Network ◽

Scale Free ◽

Free Network

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Comparing Estimation Methods for Psychometric Networks with Ordinal Data

10.31234/osf.io/ej2gn ◽

2021 ◽

Author(s):

Simran Johal ◽

Mijke Rhemtulla

Keyword(s):

Sample Size ◽

Ordinal Data ◽

Partial Correlation ◽

False Positive Rate ◽

Estimation Method ◽

Network Models ◽

Estimation Methods ◽

Edge Weight ◽

P Value ◽

Underlying Distribution

Ordinal data are extremely common in psychological research, with variables often assessed using Likert-type scales that take on only a few values. At the same time, researchers are increasingly fitting network models to ordinal item-level data. Yet very little work has evaluated how network estimation techniques perform when data are ordinal. We use a Monte Carlo simulation to evaluate and compare the performance of three estimation methods applied to either Pearson or polychoric correlations: EBIC graphical lasso with regularized edge estimates (“EBIC”), BIC model selection with partial correlation edge estimates (“BIC”), and multiple regression with p-value-based edge selection and partial correlation edge estimates (“MR”). We vary the number and distribution of thresholds, distribution of the underlying continuous data, sample size, model size, and network density, and we evaluate results in terms of model structure (sensitivity and false positive rate) and edge weight bias. Our results show that the effect of treating the data as ordinal versus continuous depends primarily on the number of levels in the data, and that estimation performance was affected by the sample size, the shape of the underlying distribution, and the symmetry of underlying thresholds. Furthermore, which estimation method is recommended depends on the research goals: MR methods tended to maximize sensitivity of edge detection, BIC approaches minimized false positives, and either one of these produced accurate edge weight estimates in sufficiently large samples. We identify some particularly difficult combinations of conditions for which no method produces stable results.

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Snowdrift Game on Topologically Alterable Complex Networks

Mathematical Problems in Engineering ◽

10.1155/2015/361270 ◽

2015 ◽

Vol 2015 ◽

pp. 1-5

Author(s):

Zhe Wang ◽

Hong Yao ◽

Jun Du ◽

Xingzhao Peng ◽

Chao Ding

Keyword(s):

Complex Networks ◽

Power Law ◽

Network Models ◽

Average Degree ◽

Clustering Coefficient ◽

Topological Parameters ◽

Snowdrift Game ◽

Cooperation Level ◽

Optimal Value ◽

Power Law Exponent

In order to study the influence of network’s structure on cooperation level of repeated snowdrift game, in the frame of two kinds of topologically alterable network models, the relation between the cooperation density and the topological parameters was researched. The results show that the network’s cooperation density is correlated reciprocally with power-law exponent and positively with average clustering coefficient; in other words, the more homogenous and less clustered a network, the lower the network’s cooperation level; and the relation between average degree and cooperation density is nonmonotonic; when the average degree deviates from the optimal value, the cooperation density drops.

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Comorbid anxiety and depression psychopathology in university students: a network approach

South African Journal of Psychology ◽

10.1177/0081246320973839 ◽

2020 ◽

pp. 008124632097383

Author(s):

Malose Makhubela

Keyword(s):

University Students ◽

Partial Correlation ◽

Clinical Care ◽

Treatment Options ◽

Network Models ◽

Network Approach ◽

Comorbid Anxiety ◽

Self Blame ◽

Symptoms Of Depression ◽

And Gender

Depression in university students is known to commonly co-occur with other mental disorders, especially anxiety. It is, however, not known how this comorbidity affects the psychopathology of depression in university students. Compared to commonly used methods, the clinical network approach provides a better framework for understanding comorbidity. Accordingly, regularized partial correlation network models were used in this study to (1) examine the severity structure of individual depressive symptoms by the level of comorbid anxiety, and (2) explore the gender differences among these symptoms in university students ( N = 919; Mage = 21 years., SD = 2.99; 72% = Female). Anhedonia, hopelessness, worthlessness, self-blame, and loneliness were the most central symptoms of depression in this study. The Network Comparison Test revealed no statistically significant global structure and strength of the depressive symptom network by comorbid anxiety level and gender. Implications of the results and network framework with regard to developing alternative treatment options, and the optimization of clinical care and assessment of depression are discussed.

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