A new method for constructing networks from binary data

Claudia D. van Borkulo; Denny Borsboom; Sacha Epskamp; Tessa F. Blanken; Lynn Boschloo; Robert A. Schoevers; Lourens J. Waldorp

doi:10.1038/srep05918

journal article Open Access Aug 01, 2014

A new method for constructing networks from binary data

Claudia D. van Borkulo

Denny Borsboom

Sacha Epskamp

Tessa F. Blanken Lynn Boschloo

Robert A. Schoevers

Lourens J. Waldorp

Scientific Reports Vol. 4 No. 1 · Springer Science and Business Media LLC

View at Publisher Save 10.1038/srep05918

Topics

No keywords indexed for this article. Browse by subject →

References

53

[1]

Barabási, A. L. The network takeover. Nat. Phys. 8, 14–16 (2012). 10.1038/nphys2188

[2]

Barzel, B. & Barabási, A. L. Universality in network dynamics. Nat. Phys. 9, 673–681 (2013). 10.1038/nphys2741

[3]

Kitsak, M. et al. Identification of influential spreaders in complex networks. Nat. Phys. 6, 888–893 (2010). 10.1038/nphys1746

[4]

Liu, Y. Y., Slotine, J. J. & Barabási, A.-L. Controllability of complex networks. Nature 473, 167–173 (2011). 10.1038/nature10011

[5]

Vespignani, A. Modelling dynamical processes in complex socio-technical systems. Nat. Phys. 8, 32–39 (2012). 10.1038/nphys2160

[6]

Borsboom, D. Psychometric perspectives on diagnostic systems. J. Clin. Psychol. 64, 1089–1108 (2008). 10.1002/jclp.20503

[7]

Network Analysis: An Integrative Approach to the Structure of Psychopathology

Denny Borsboom, Angélique O.J. Cramer

Annual Review of Clinical Psychology 2013 10.1146/annurev-clinpsy-050212-185608

[8]

Comorbidity: A network perspective

Angélique O. J. Cramer, Lourens J. Waldorp, Han L. J. van der Maas et al.

Behavioral and Brain Sciences 2010 10.1017/s0140525x09991567

[9]

Deconstructing the construct: A network perspective on psychological phenomena

Verena D. Schmittmann, Angélique O.J. Cramer, Lourens J. Waldorp et al.

New Ideas in Psychology 2011 10.1016/j.newideapsych.2011.02.007

[10]

Van Der Maas, H. L. J. et al. A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychol. Rev. 113, 842 (2006). 10.1037/0033-295x.113.4.842

[11]

Schäfer, J. & Strimmer, K. A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat. Appl. Genet. Molec. Biol. 4, 32 (2005). 10.2202/1544-6115.1175

[12]

Bickel, P. J. & Levina, E. Covariance regularization by thresholding. Ann. Stat. 36, 2577–2604 (2008). 10.1214/08-aos600

[13]

Sparse inverse covariance estimation with the graphical lasso

Jerome Friedman, Trevor Hastie, Robert Tibshirani

Biostatistics 2008 10.1093/biostatistics/kxm045

[14]

Kalisch, M., Mächler, M., Colombo, D., Maathuis, M. H. & Bühlmann, P. Causal inference using graphical models with the r package pcalg. J. Stat. Softw. 47, 1–26 (2012). 10.18637/jss.v047.i11

[15]

Spirtes, P., Glymour, C. & Scheines, R. Causation, Prediction and Search (MIT press, Cambrigde, Massachusetts, 2001). 10.7551/mitpress/1754.001.0001

[16]

Drton, M. & Perlman, M. Multiple testing and error control in gaussian graphical model selection. Statist. Sci. 22, 430–449 (2007). 10.1214/088342307000000113

[17]

Efron, B. Large-scale simultaneous hypothesis testing: The choice of a null hypothesis. J. Am. Statist. Assoc. 99, 96–104 (2004). 10.1198/016214504000000089

[18]

fdrtool: a versatile R package for estimating local and tail area-based false discovery rates

Korbinian Strimmer

Bioinformatics 2008 10.1093/bioinformatics/btn209

[19]

Kindermann, R. & Snell, J. L. Markov Random Fields and their Applications, vol. 1 (American Mathematical Society Providence, RI, 1980). 10.1090/conm/001/01

[20]

Graphical Models

Steffen L Lauritzen

10.1093/oso/9780198522195.001.0001

[21]

Gaussian Markov Distributions over Finite Graphs

T. P. Speed, H. T. Kiiveri

The Annals of Statistics 1986 10.1214/aos/1176349846

[22]

Foygel, R. & Drton, M. Extended bayesian information criteria for gaussian graphical models. Adv. Neural Inf. Process. Syst. 23, 2020–2028 (2010).

[23]

Ravikumar, P., Wainwright, M. J., Raskutti, G. & Yu, B. et al. High-dimensional covariance estimation by minimizing l1-penalized log-determinant divergence. Electron. J. Statist. 5, 935–980 (2011). 10.1214/11-ejs631

[24]

Regression Shrinkage and Selection Via the Lasso

Robert Tibshirani

Journal of the Royal Statistical Society Series B:... 1996 10.1111/j.2517-6161.1996.tb02080.x

[25]

High-dimensional graphs and variable selection with the Lasso

Nicolai Meinshausen, Peter Bühlmann

The Annals of Statistics 2006 10.1214/009053606000000281

[26]

High-dimensional Ising model selection using ℓ1-regularized logistic regression

Pradeep Ravikumar, Martin J. Wainwright, John D. Lafferty

The Annals of Statistics 2010 10.1214/09-aos691

[27]

Ising, E. Beitrag zur theorie des ferromagnetismus. Z. Phys. A-Hadrons. Nucl. 31, 253–258 (1925).

[28]

Extended Bayesian information criteria for model selection with large model spaces

J. Chen, Z. Chen

Biometrika 2008 10.1093/biomet/asn034

[29]

Foygel, R. & Drton, M. High-dimensional ising model selection with bayesian information criteria. arXiv preprint arXiv:1403.3374 (2014).

[30]

Emergence of Scaling in Random Networks

Albert-László Barabási, Réka Albert

Science 1999 10.1126/science.286.5439.509

[31]

On random graphs. I.

P. Erdős, A. Rényi

Publicationes Mathematicae Debrecen 1959 10.5486/pmd.1959.6.3-4.12

[32]

Collective dynamics of ‘small-world’ networks

Duncan J. Watts, Steven H. Strogatz

Nature 1998 10.1038/30918

[33]

Foygel, R. & Drton, M. Bayesian model choice and information criteria in sparse generalized linear models. arXiv preprint arXiv:1112.5635 (2011).

[34]

Jardine, N. & van Rijsbergen, C. J. The use of hierarchic clustering in information retrieval. Inform. Storage Ret. 7, 217–240 (1971). 10.1016/0020-0271(71)90051-9

[35]

Rush, A. et al. The inventory of depressive symptomatology (IDS): Psychometric properties. Psychol. Med. 26, 477–486 (1996). 10.1017/s0033291700035558

[36]

Penninx, B. W. et al. The netherlands study of depression and anxiety (NESDA): Rationale, objectives and methods. Int. J. Method. Psych. 17, 121–140 (2008). 10.1002/mpr.256

[37]

Barrat, A., Barthelemy, M., Pastor-Satorras, R. & Vespignani, A. The architecture of complex weighted networks. Proc. Natl. Acad. Sci. U.S.A. 101, 3747–3752 (2004). 10.1073/pnas.0400087101

[38]

Complex networks: Structure and dynamics

S BOCCALETTI, V LATORA, Y MORENO et al.

Physics Reports 2006 10.1016/j.physrep.2005.10.009

[39]

Node centrality in weighted networks: Generalizing degree and shortest paths

Tore Opsahl, Filip Agneessens, John Skvoretz

Social Networks 2010 10.1016/j.socnet.2010.03.006

[40]

Goldberg, D. & Fawcett, J. The importance of anxiety in both major depression and bipolar disorder. Depress. Anxiety 29, 471–478 (2012). 10.1002/da.21939

[41]

Kessler, R. C., Nelson, C. B., McGonagle, K. A. & Liu, J. et al. Comorbidity of DSM-III—R major depressive disorder in the general population: Results from the US National Comorbidity Survey. Br. J. Psychiatry 30, 17–30 (1996). 10.1192/s0007125000298371

[42]

Schoevers, R. A., Beekman, A. T. F., Deeg, D. J. H., Jonker, C. & Van Tilburg, W. Comorbidity and risk-patterns of depression, generalised anxiety disorder and mixed anxiety-depression in later life: results from the amstel study. Int. J. Geriatr. 18, 994–1001 (2003). 10.1002/gps.1001

[43]

American Psychiatric Association. . The Diagnostic and Statistical Manual of Mental Disorders (5th ed.) (Arlington, VA: American Psychiatric Publishing, 2013). 10.1176/appi.books.9780890425596

[44]

Orman, G. K. & Labatut, V. A Comparison of Community Detection Algorithms on Artificial Networks (Springer, Berlin Heidelberg, 2009). 10.1007/978-3-642-04747-3_20

[45]

Computing Communities in Large Networks Using Random Walks

Pascal Pons, Matthieu Latapy

Journal of Graph Algorithms and Applications 2006 10.7155/jgaa.00124

[46]

Wu, F.-Y. The potts model. Rev. Mod. Phys. 54, 235 (1982). 10.1103/revmodphys.54.235

[47]

Loh, P. L. & Wainwright, M. J. Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses. Ann. Stat. 41, 3022–3049 (2013). 10.1214/13-aos1162

[48]

Regularization Paths for Generalized Linear Models via Coordinate Descent

Jerome Friedman, Trevor Hastie, Robert Tibshirani

Journal of Statistical Software 2010 10.18637/jss.v033.i01

[49]

qgraph: Network Visualizations of Relationships in Psychometric Data

Sacha Epskamp, Angélique O. J. Cramer, Lourens J. Waldorp et al.

Journal of Statistical Software 2012 10.18637/jss.v048.i04

[50]

Monte Carlo sampling methods using Markov chains and their applications

W. K. Hastings

Biometrika 1970 10.1093/biomet/57.1.97

Showing 50 of 53 references

Cited By

539

Diminished anticipatory-consummatory pleasure interplay in high schizotypal traits and subthreshold depression: potential risk for schizophrenia and depression

Lei Shen, Chi Xie · 2026

Schizophrenia

Network analysis of item-level neuroticism in recurrent major depression among Korean women

Hyeona Yu, Jungkyu Park · 2026

Journal of Affective Disorders

Graphical Ising Models for Missing Data Patterns Detection in Sustainability Surveys

Andrea Mecca, Anna Gottard · 2026

Social Indicators Research

A Network Analysis of Panic Disorder, Agoraphobia, and Generalized Anxiety Disorder in 463 Patients From a Psychiatric Hospital

Emanuela Pizzolla, Juan Martin Tecco · 2026

Brain and Behavior

Burden of multiple high-risk factors in pregnancy before and after the universal two-child policy in Chinese women: An observational study

Yue Zhang, Weijie Ding · 2024

Journal of Global Health

Testing the validity of the broaden-and build theory of positive emotions: a network analytic approach

Leopold Helmut Otto Roth, Celine Bencker · 2024

Frontiers in Psychology

The Predictive Role of Tolerance and Health Problems in Problem Gambling: A Cross-Sectional and Cross-Lagged Network Analyses

Zsolt Horváth, Borbála Paksi · 2023

Journal of Gambling Studies

The structure of menopausal syndrome: Using network analysis to understand unique symptomatic relationships

Di Zhao, Gaorong Lv · 2022

International Journal of Gynecology...

Early Warning Signals in Phase Space: Geometric Resilience Loss Indicators From Multiplex Cumulative Recurrence Networks

Fred Hasselman · 2022

Frontiers in Physiology

Network Analysis of Outpatients to Identify Predictive Symptoms and Combinations of Symptoms Associated With Positive/Negative SARS-CoV-2 Nasopharyngeal Swabs

Hervé Spechbach, Frédérique Jacquerioz · 2021

Frontiers in Medicine

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data

Sacha Epskamp, Lourens J. Waldorp · 2018

Multivariate Behavioral Research

Estimating the dimensionality of intelligence like data using Exploratory Graph Analysis

Hudson F. Golino, Andreas Demetriou · 2017

Intelligence

How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets

J. M. B. Haslbeck, E. I. Fried · 2017

Psychological Medicine

Metrics

539

Citations

53

References

Details

Published: Aug 01, 2014
Vol/Issue: 4(1)
License: View

Authors

C

Claudia D. van Borkulo

D

Denny Borsboom

Department of Psychology, University of Amsterdam

S

Sacha Epskamp

Department of Psychology, University of Amsterdam, Amsterdam, Netherlands

Cite This Article

Claudia D. van Borkulo, Denny Borsboom, Sacha Epskamp, et al. (2014). A new method for constructing networks from binary data. Scientific Reports, 4(1). https://doi.org/10.1038/srep05918

A new method for constructing networks from binary data

You May Also Like