Back to the basics: Rethinking partial correlation network methodology

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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Estimating Factors with Psychometric Networks: A Monte Carlo Simulation Comparing Community Detection Algorithms

10.31234/osf.io/hz89e ◽

2020 ◽

Author(s):

Alexander P. Christensen ◽

Hudson Golino

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Community Detection ◽

Partial Correlation ◽

Estimation Method ◽

Detection Algorithm ◽

Systematic Investigation ◽

Correlation Network ◽

Detection Algorithms ◽

Network Estimation

Estimating the number of factors in multivariate data is at the crux of psychological measurement. Factor analysis has a long tradition in the field but it’s been challenged recently by exploratory graph analysis (EGA), an approach based on network psychometrics. EGA first estimates a regularized partial correlation network using the graphical least absolute shrinkage and selection operator (GLASSO), and then applies the Walktrap community detection algorithm, which identifies communities (or factors) in the network. Simulation studies have demonstrated that EGA has comparable or better accuracy than contemporary state-of-the-art factor analytic methods (e.g., parallel analysis), while providing some additional advantages such as not requiring rotations and deterministic allocation of items into factors. Despite EGA’s effectiveness, there has yet to be an investigation into whether other community detection algorithms could achieve equivalent or better perfomance. In the present study, we performed a Monte Carlo simulation using the GLASSO and two variants of a non-regularized partial correlation network estimation method and several community detection algorithms in the open-source igraph package in R. The purpose of the present study was to critically examine whether the network estimation and community detection components of EGA are optimal for estimating factors in psychological data as well as to provide a systematic investigation into how different community detection algorithms perform “out-of-the-box.” The results indicate that the Fast-greedy, Louvain, and Walktrap algorithms paired with the GLASSO method were consistently among the most accurate and least biased across conditions.

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On the Consequences of Power-Law Behavior in Partial Correlation Network Models

SSRN Electronic Journal ◽

10.2139/ssrn.2892530 ◽

2017 ◽

Cited By ~ 1

Author(s):

Matteo Barigozzi ◽

Christian T. Brownlees ◽

Gabor Lugosi

Keyword(s):

Power Law ◽

Partial Correlation ◽

Network Models ◽

Correlation Network

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Exploring Young Adulthood Psychopathology Networks Related to Depression Using Partial Correlation Network and Bayesian Network

10.21203/rs.3.rs-131241/v1 ◽

2021 ◽

Author(s):

Min Seob Kim ◽

Bumseok Jeong

Keyword(s):

Network Analysis ◽

Bayesian Network ◽

Young Adulthood ◽

Partial Correlation ◽

Health Management ◽

Verbal Abuse ◽

Correlation Network ◽

Traumatic Experiences ◽

Subjective Distress ◽

Disease Networks

Abstract To characterize young adulthood depression is complicated because it is entangled with a broad spectrum of symptoms as well as traumatic experiences during development. However, previous symptom network studies have focused on undirected transdiagnostic association among depression and anxiety symptoms. Our study investigated both undirected and directed connections among variables potentially associated with depression, such as anxiety, addiction, subjective distress caused by traumatic events, perceived emotional adversities, and support systems. Both the regularized partial correlation network analysis and Bayesian network analysis were applied to 579 subjects screened for depression. Anxiety-related symptoms played a role as a hub node in the partial correlation network and Bayesian network. The vulnerability analysis of the partial correlation network showed that verbal abuse, social anxiety, concentration problems, and suicidal ideation had the strongest influence on changes in the network’s topology. In the Bayesian network analysis, loss of interest, depressed mood, and parental verbal abuse were located as parent nodes in the directed acyclic graph. In the aspect of disease networks, more attention should be paid to certain variables encompassing various domains as well as depressive symptoms in young adults’ mental health management.

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