A new method for constructing networks from binary data

Scientific Reports

Volumn 4, Issue , 2014, Pages

  Van Borkulo, Claudia D ^a,b   Borsboom, Denny ^b   Epskamp, Sacha ^b   Blanken, Tessa F ^b   Boschloo, Lynn ^a   Schoevers, Robert A ^a   Waldorp, Lourens J ^b  

^a UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

^b UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; CASE CONTROL STUDY; COMPUTER PROGRAM; COMPUTER SIMULATION; DEPRESSION; HUMAN; PSYCHOLOGY; THEORETICAL MODEL;

ALGORITHMS; CASE-CONTROL STUDIES; COMPUTER SIMULATION; DEPRESSION; HUMANS; MODELS, THEORETICAL; SOFTWARE;

EID: 84905492859     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep05918     Document Type: Article

References (53)

1. Barabási A. L. The network takeover. Nat. Phys. 8 14-16 (2012
2. Barzel B.& Barabási A. L. Universality in network dynamics. Nat. Phys. 9 673-681 (2013
3. Kitsak M. et al. Identification of influential spreaders in complex networks. Nat. Phys. 6 888-893 (2010
4. Liu Y. Y. Slotine J. J.& Barabási A.-L. Controllability of complex networks. Nature 473 167-173 (2011
5. Vespignani A. Modelling dynamical processes in complex socio-technical systems. Nat. Phys. 8 32-39 (2012
6. Borsboom D. Psychometric perspectives on diagnostic systems. J. Clin. Psychol. 64 1089-1108 (2008
7. Borsboom,D.&Cramer A. O. J. Network analysis: An integrative approach to the structure of psychopathology. Annu. Rev. Clin. Psychol. 9 91-121 (2013
8. Cramer A. O. J. Waldorp L. J. Van Der Maas H. L. J.& Borsboom D. Comorbidity: A network perspective. Behav. Brain Sci. 33 137-150 (2010
9. Schmittmann,V. D. et al.Deconstructing the construct: A network perspective on psychological phenomena. New Ideas Psychol. 31 43-53 (2011
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
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
12. Bickel P. J.& Levina E. Covariance regularization by thresholding. Ann. Stat. 36 2577-2604 (2008
13. Friedman J. Hastie T.& Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9 432-441 (2008 (Pubitemid 351882084)
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
15. Spirtes P. Glymour C.& Scheines R. Causation Prediction and Search (MIT press Cambrigde Massachusetts 2001
16. Drton M.& Perlman,M.Multiple testing and error control in gaussian graphical model selection. Statist. Sci. 22 430-449 (2007
17. Efron B. Large-scale simultaneous hypothesis testing: The choice of a null hypothesis. J. Am. Statist. Assoc. 99 96-104 (2004 (Pubitemid 38545197)
18. Strimmer K. fdrtool: a versatile r package for estimating local and tail area-based false discovery rates. Bioinformatics 24 1461-1462 (2008 (Pubitemid 351851002)
19. Kindermann R.& Snell J. L. Markov Random Fields and their Applications vol. 1 (American Mathematical Society Providence RI 1980
20. Lauritzen S. Graphical Models (Oxford University Press USA 1996
21. Speed T.& Kiiveri H. Gaussian markov distributions over finite graphs. Ann. Stat. 14 138-150 (1986
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
24. Tibshirani R. Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. Ser. B 58 267-288 (1996
25. Meinshausen,N.&Bühlmann P.High-dimensional graphs and variable selection with the lasso. Ann. Stat. 34 1436-1462 (2006 (Pubitemid 44231168)
26. Ravikumar P.,Wainwright M. J.& Lafferty J. D. High-dimensional ising model selection using l1-regularized logistic regression. Ann. Stat. 38 1287-1319 (2010
27. Ising E. Beitrag zur theorie des ferromagnetismus. Z. Phys. A-Hadrons. Nucl. 31 253-258 (1925
28. Chen J.& Chen Z. Extended bayesian information criteria for model selection with large model spaces. Biometrika 95 759-771 (2008
29. Foygel R.& Drton M. High-dimensional ising model selection with bayesian information criteria. arXiv preprint arXiv:1403.3374 (2014
30. Barabási A. L.&Albert R. Emergence of scaling in random networks. Science 286 509-512 (1999
31. Erdös P.&Rényi A. On random graphs. Publ. Math. Debrecen 6 290-297 (1959
32. Watts D. J.& Strogatz S. H. Collective dynamics of 'small-world' networks. Nature 393 440-442 (1998 (Pubitemid 28292183)
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
35. Rush A. et al. The inventory of depressive symptomatology (IDS): Psychometric properties. Psychol. Med. 26 477-486 (1996 (Pubitemid 26142491)
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
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 (Pubitemid 38381049)
38. Boccaletti S. Latora V.,Moreno Y. Chavez M.&Hwang,D. Complex networks: Structure and dynamics. Phys. Rep. 424 175-308 (2006 (Pubitemid 43135890)
39. Opsahl T. Agneessens F.& Skvoretz J. Node centrality in weighted networks: Generalizing degree and shortest paths. Soc. Networks 32 245-251 (2010
40. Goldberg D.& Fawcett J. The importance of anxiety in both major depression and bipolar disorder. Depress. Anxiety 29 471-478 (2012
41. Kessler R. C.,Nelson C. B.,McGonagle K. A.&Liu J. et al.Comorbidity of DSMIII- R major depressive disorder in the general population: Results from the US National Comorbidity Survey. Br. J. Psychiatry 30 17-30 (1996 (Pubitemid 26000488)
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 (Pubitemid 37479371)
43. American Psychiatric Association. The Diagnostic and Statistical Manual of Mental Disorders (5th ed.) (Arlington VA: American Psychiatric Publishing 2013
44. Orman G. K.& Labatut V. A Comparison of Community Detection Algorithms on Artificial Networks (Springer Berlin Heidelberg 2009
45. Pons P.& Latapy M. Computing communities in large networks using random walks. J. Graph Algorithms Appl. 10 191-218 (2006
46. Wu F.-Y. The potts model. Rev. Mod. Phys. 54 235 (1982
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
48. Friedman J. Hastie T.& Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33 1 (2010
49. Epskamp S. Cramer A. O. J. Waldorp L. J. Schmittmann V.D.&Borsboom,D. qgraph: Network visualizations of relationships in psychometric data. J. Stat. Softw. 48 1-18 (2012
50. Hastings W. K. Monte carlo sampling methods using markov chains and their applications. Biometrika 57 97-109 (1970
51. Epskamp S. IsingSampler: Sampling methods and distribution functions for the Ising model (2013). URL github.com/SachaEpskamp/IsingSampler. R package version 0.1.
52. Murray I. Advances in Markov chain Monte Carlo methods. PhD thesis Gatsby Computational Neuroscience Unit University College London (2007
53. Fruchterman T. M.& Reingold E. M. Graph drawing by force-directed placement. Software Pract. Exper. 21 1129-1164 (1991).