Community Detection in Partial Correlation Network Models

2016 ◽  
Author(s):  
Christian T. Brownlees ◽  
Gabor Lugosi
Keyword(s):  
Community Detection ◽  
Partial Correlation ◽  
Network Models ◽  
Correlation Network

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

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 Power-law partial correlation network models

2018 ◽  
Vol 12 (2) ◽  
pp. 2905-2929 ◽  
Author(s):  
Matteo Barigozzi ◽  
Christian Brownlees ◽  
Gábor Lugosi
Keyword(s):  
Power Law ◽  
Partial Correlation ◽  
Network Models ◽  
Correlation Network

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

scholarly journals Community Detection in Multiplex Networks

2021 ◽  
Vol 54 (3) ◽  
pp. 1-35
Author(s):  
Matteo Magnani ◽  
Obaida Hanteer ◽  
Roberto Interdonato ◽  
Luca Rossi ◽  
Andrea Tagarelli
Keyword(s):  
Community Detection ◽  
Experimental Evaluation ◽  
Network Models ◽  
Ground Truth ◽  
Community Structures ◽  
Multiplex Networks ◽  
Detection Algorithms ◽  
Multiplex Network ◽  
The Right ◽  
Modes Of Interaction

A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.

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 The item network and domain network of burnout in Chinese nurses

BMC Nursing ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Lin Wu ◽  
Lei Ren ◽  
Yifei Wang ◽  
Kan Zhang ◽  
Peng Fang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Community Detection ◽  
Emotional Exhaustion ◽  
Network Structure ◽  
Partial Correlation ◽  
Clinical Intervention ◽  
Maslach Burnout Inventory ◽  
Cluster Sampling ◽  
Occupational Burnout ◽  
Nurse Burnout

Abstract Background As a common social phenomenon, nurses’ occupational burnout has a high incidence rate, which seriously affects their mental health and nursing level. The current assessment mostly uses the total score model and explores the influence of external factors on burnout, while the correlation between burnout items or dimensions is less explored. Ignoring the correlation between the items or dimensions may result in a limited understanding of nurse occupational burnout. This paper explores the item and dimension network structure of the Maslach Burnout Inventory-General Survey (MBI-GS) in Chinese nurses, so as to gain a deeper understanding of this psychological construct and identify potential targets for clinical intervention. Methods A total of 493 Chinese nurses were recruited by cluster sampling. All participants were invited to complete the survey on symptoms of burnout. Network analysis was used to investigate the item network of MBI-GS. In addition, community detection was used to explore the communities of MBI-GS, and then network analysis was used to investigate the dimension network of MBI-GS based on the results of community detection. Regularized partial correlation and non-regularized partial correlation were used to describe the association between different nodes of the item network and dimension network, respectively. Expected influence and predictability were used to describe the relative importance and the controllability of nodes in both the item and dimension networks. Results In the item network, most of the strongly correlated edges were in the same dimension of emotional exhaustion (E), cynicism (C) and reduced professional efficacy (R), respectively. E5 (Item 5 of emotional exhaustion, the same below) “I feel burned out from my work”, C1 “I have become more callous toward work since I took this job”, and R3 “In my opinion, I am good at my job” had the highest expected influence (z-scores = 0.99, 0.81 and 0.94, respectively), indicating theirs highest importance in the network. E1 “I feel emotionally drained from my work” and E5 had the highest predictability (E1 = 0.74, E5 = 0.74). It shows that these two nodes can be interpreted by their internal neighbors to the greatest extent and have the highest controllability in the network. The spinglass algorithm and walktrap algorithm obtained exactly the same three communities, which are consistent with the original dimensions of MBI-GS. In the dimension network, the emotional exhaustion dimension was closely related to the cynicism dimension (weight = 0.65). Conclusions The network model is a useful tool to study burnout in Chinese nurses. This study explores the item and domain network structure of nurse burnout from the network perspective. By calculating the relevant indicators, we found that E5, C1, and R3 were the most central nodes in the item network and cynicism was the central node in the domain network, suggesting that interventions aimed at E5, C1, R3 and cynicism might decrease the overall burnout level of Chinese nurses to the greatest extent. This study provides potential targets and a new way of thinking for the intervention of nurse burnout, which can be explored and verified in clinical practice.

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