Quantifying Influence in Financial Markets via Partial Correlation Network Inference

2019 ◽  
Author(s):  
Tristan Millington ◽  
Mahesan Niranjan
Keyword(s):  
Financial Markets ◽  
Partial Correlation ◽  
Network Inference ◽  
Correlation Network

A network analysis of political incivility dimensions

2019 ◽  
Vol 4 (3) ◽  
pp. 204-223
Author(s):  
Toby Hopp
Keyword(s):  
Network Analysis ◽  
Partial Correlation ◽  
Precise Definition ◽  
Correlation Network ◽  
Political Discussion ◽  
Relational Dynamics ◽  
Communication Processes ◽  
Democratic Communication ◽  
Scholarly Inquiry

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.

scholarly journals A strategy to incorporate prior knowledge into correlation network cutoff selection

2019 ◽  
Author(s):  
Elisa Benedetti ◽  
Maja Pučić-Baković ◽  
Toma Keser ◽  
Nathalie Gerstner ◽  
Mustafa Büyüközkan ◽  
...  
Keyword(s):  
Sample Size ◽  
Prior Knowledge ◽  
Prior Information ◽  
Network Inference ◽  
Correlation Coefficients ◽  
Ground Truth ◽  
The Cancer Genome Atlas ◽  
Correlation Network ◽  
Biological Interactions ◽  
Optimal Network

AbstractCorrelation networks are commonly used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the significance of the underlying correlation coefficients. A statistical cutoff, however, is not guaranteed to capture biological reality, and heavily depends on dataset properties such as sample size. We here propose an alternative, innovative approach to address the problem of network reconstruction. Specifically, we developed a cutoff selection algorithm that maximizes the agreement to a given ground truth. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. The optimal network outperforms networks obtained with statistical cutoffs and is robust with respect to sample size. Importantly, we can show that even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach on an untargeted metabolomics and a transcriptomics dataset from The Cancer Genome Atlas (TCGA). For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for the optimization. Overall, this paper shows that using prior information for correlation network inference is superior to using regular statistical cutoffs, even if the prior information is incomplete or partially inaccurate.

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

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.

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

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.

scholarly journals Biological network inference using low order partial correlation

Methods ◽  
2014 ◽  
Vol 69 (3) ◽  
pp. 266-273 ◽  
Author(s):  
Yiming Zuo ◽  
Guoqiang Yu ◽  
Mahlet G. Tadesse ◽  
Habtom W. Ressom
Keyword(s):  
Partial Correlation ◽  
Network Inference ◽  
Biological Network ◽  
Low Order

Community Detection in Partial Correlation Network Models

2020 ◽  
pp. 1-11
Author(s):  
Christian Brownlees ◽  
Guðmundur Stefán Guðmundsson ◽  
Gábor Lugosi
Keyword(s):  
Community Detection ◽  
Partial Correlation ◽  
Network Models ◽  
Correlation Network
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