*Result*: Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation.
Title:
Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation.
Authors:
Christensen AP; Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, 37203, USA. alexpaulchristensen@gmail.com., Garrido LE; Pontificia Universidad Católica Madre y Maestra, Santiago De Los Caballeros, Dominican Republic., Guerra-Peña K; Pontificia Universidad Católica Madre y Maestra, Santiago De Los Caballeros, Dominican Republic., Golino H; University of Virginia, Charlottesville, USA.
Source:
Behavior research methods [Behav Res Methods] 2024 Mar; Vol. 56 (3), pp. 1485-1505. Date of Electronic Publication: 2023 Jun 02.
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Springer Country of Publication: United States NLM ID: 101244316 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1554-3528 (Electronic) Linking ISSN: 1554351X NLM ISO Abbreviation: Behav Res Methods Subsets: MEDLINE
Imprint Name(s):
Publication: 2010- : New York : Springer
Original Publication: Austin, Tex. : Psychonomic Society, c2005-
Original Publication: Austin, Tex. : Psychonomic Society, c2005-
MeSH Terms:
References:
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008. (PMID: 10.1088/1742-5468/2008/10/P10008)
Brusco, M. J., Steinley, D., & Watts, A. L. (2022). On maximization of the modularity index in network psychometrics. Behavior Research Methods, 1–17. https://doi.org/10.3758/s13428-022-01975-5.
Brusco, M., Steinley, D., & Watts, A. L. (2021). Spectral clustering of psychological networks. https://doi.org/10.31234/osf.io/fjqd4.
Brusco, M., Steinley, D., & Watts, A. L. (2022). A comparison of spectral clustering and the walktrap algorithm for community detection in network psychometrics. Psychological Methods. https://doi.org/10.1037/met0000509. (PMID: 10.1037/met000050935797161)
Castro, D., Ferreira, F., & Ferreira, T. B. (2020). Modularity of the personality network. European Journal of Psychological Assessment, 36, 998–1008. https://doi.org/10.1027/1015-5759/a000613. (PMID: 10.1027/1015-5759/a000613)
Cattell, R. B. (1978). The scientific use of factor analysis in behavioral and life sciences. Boston: Springer. https://doi.org/10.1007/978-1-4684-2262-7. (PMID: 10.1007/978-1-4684-2262-7)
Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., ... Borges, B. (2022). shiny: Web Application Framework for R. Retrieved from https://CRAN.R-project.org/package=shiny.
Chen, J., & Chen, Z. (2008). Extended bayesian information criteria for model selection with large model spaces. Biometrika, 95, 759–771. https://doi.org/10.1093/biomet/asn034. (PMID: 10.1093/biomet/asn034)
Christensen, A. P., & Golino, H. (2021). Estimating the stability of psychological dimensions via bootstrap exploratory graph analysis: A Monte Carlo simulation and tutorial. Psych, 3(3), 479–500. https://doi.org/10.3390/psych3030032. (PMID: 10.3390/psych3030032)
Clauset, A., Newman, M. E. J., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70, 066111. https://doi.org/10.1103/PhysRevE.70.066111. (PMID: 10.1103/PhysRevE.70.066111)
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. https://doi.org/10.1037/0033-2909.112.1.155. (PMID: 10.1037/0033-2909.112.1.15519565683)
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695, 1–9. Retrieved from https://www.semanticscholar.org/paper/The-igraph-software-package-for-complex-network-Cs/%C3/%A1rdi-Nepusz/1d2744b83519657f5f2610698a8ddd177ced4f5c?p2df.
De Beurs, D., Fried, E. I., Wetherall, K., Cleare, S., O’Connor, D. B., Ferguson, E., ... O’Connor, R. C. (2019). Exploring the psychology of suicidal ideation: A theory driven network analysis. Behaviour Research and Therapy, 120, 103419. https://doi.org/10.1016/j.brat.2019.103419.
Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48, 1–18. https://doi.org/10.18637/jss.v048.i04.
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23, 617–634. https://doi.org/10.1037/met0000167. (PMID: 10.1037/met000016729595293)
Epskamp, S., Kruis, J., & Marsman, M. (2017). Estimating psychopathological networks: Be careful what you wish for. PLoS ONE, 12(6). https://doi.org/10.1371/journal.pone.0179891.
Epskamp, S., Maris, G., Waldrop, L. J., & Borsboom, D. (2018). Network psychometrics. In P. Irwing, D. Hughes, & T. Booth (Eds.), The Wiley handbook of psychometric testing, 2 volume set: A multidisciplinary reference on survey, scale and test development. New York, NY: Wiley. https://doi.org/10.1002/9781118489772.ch30.
Fan, Y., Li, M., Zhang, P., Wu, J., & Di, Z. (2007). Accuracy and precision of methods for community identification in weighted networks. Physica A: Statistical Mechanics and Its Applications, 377, 363–372. https://doi.org/10.1016/j.physa.2006.11.036. (PMID: 10.1016/j.physa.2006.11.036)
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486, 75–174. https://doi.org/10.1016/j.physrep.2009.11.002. (PMID: 10.1016/j.physrep.2009.11.002)
Fortunato, S., & Barthelemy, M. (2007). Resolution limit in community detection. Proceedings of the National Academy of Sciences, 104(1), 36–41. https://doi.org/10.1073/pnas.0605965104. (PMID: 10.1073/pnas.0605965104)
Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models. In J. D. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, & A. Culotta (Eds.), Advances in neural information processing systems (pp. 604–612). Retrieved from https://papers.nips.cc/paper/4087-extended-bayesian-information-criteria-for-gaussian-graphical-models.
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 35–41. Retrieved from https://www.jstor.org/stable/3033543.
Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9, 432–441. https://doi.org/10.1093/biostatistics/kxm045. (PMID: 10.1093/biostatistics/kxm04518079126)
Friedman, J., Hastie, T., & Tibshirani, R. (2014). glasso: Graphical lasso – estimation of Gaussian graphical models. Retrieved from https://CRAN.R-project.org/package=glasso.
Garrido, L. E., Abad, F. J., & Ponsoda, V. (2011). Performance of Velicer’s minimum average partial factor retention method with categorical variables. Educational and Psychological Measurement, 71, 551–570. https://doi.org/10.1177/0013164410389489. (PMID: 10.1177/0013164410389489)
Garrido, L. E., Abad, F. J., & Ponsoda, V. (2013). A new look at Horn’s parallel analysis with ordinal variables. Psychological Methods, 18, 454–474. https://doi.org/10.1037/a0030005. (PMID: 10.1037/a003000523046000)
Gates, K. M., Henry, T., Steinley, D., & Fair, D. A. (2016). A Monte Carlo evaluation of weighted community detection algorithms. Frontiers in Neuroinformatics, 10, 45. https://doi.org/10.3389/fninf.2016.00045. (PMID: 10.3389/fninf.2016.00045278910875102890)
Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99, 7821–7826. https://doi.org/10.1073/pnas.122653799. (PMID: 10.1073/pnas.122653799)
Golino, H., & Epskamp, S. (2017). Exploratory Graph Analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE, 12, e0174035. https://doi.org/10.1371/journal.pone.0174035. (PMID: 10.1371/journal.pone.0174035285948395465941)
Golino, H., Moulder, R., Shi, D., Christensen, A. P., Neito, M. D., Nesselroade, J. R., Boker, & S. M. (2020). Entropy Fit Index: A new fit measure for assessing the structure and dimensionality of multiple latent variables. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2020.1779642.
Golino, H., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Sadana, R., ... Martinez-Molina, A. (2020). Investigating the performance of Exploratory Graph Analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychological Methods, 25, 292–320. https://doi.org/10.1037/met0000255.
Guttman, L. (1953). Image theory for the structure of quantitative variates. Psychometrika, 18, 277–296. (PMID: 10.1007/BF02289264)
Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66, 393–416. https://doi.org/10.1177/0013164405282485. (PMID: 10.1177/0013164405282485)
Hoffman, M., Steinley, D., Gates, K. M., Prinstein, M. J., & Brusco, M. J. (2018). Detecting clusters/communities in social networks. Multivariate Behavioral Research, 53(1), 57–73. https://doi.org/10.1080/00273171.2017.1391682. (PMID: 10.1080/00273171.2017.139168229220584)
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185. https://doi.org/10.1007/BF02289447. (PMID: 10.1007/BF0228944714306381)
Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193–218. https://doi.org/10.1007/BF01908075. (PMID: 10.1007/BF01908075)
Isvoranu, A.-M., & Epskamp, S. (2021). Which estimation method to choose in network psychometrics? Deriving guidelines for applied researchers. Psychological Methods https://doi.org/10.1037/met0000439.
Jiménez, M., Abad, F. J., Garcia-Garzon, E., Golino, H., Christensen, A. P., & Garrido, L. E. (2022). Dimensionality assessment in generalized bi-factor structures: A network psychometrics approach. PsyArXiv. https://doi.org/10.31234/osf.io/2ujdk.
Kotov, R., Krueger, R. F., Watson, D., Achenbach, T. M., Althoff, R. R., Bagby, R. M., ... Zimmerman, M. (2017). The hierarchical taxonomy of psychopathology (HiTOP): A dimensional alternative to traditional nosologies. Journal of Abnormal Psychology, 126, 454–477. https://doi.org/10.1037/abn0000258.
Lancichinetti, A., & Fortunato, S. (2009). Community detection algorithms: A comparative analysis. Physical Review E, 80, 056117. https://doi.org/10.1103/PhysRevE.80.056117. (PMID: 10.1103/PhysRevE.80.056117)
Lancichinetti, A., & Fortunato, S. (2012). Consensus clustering in complex networks. Scientific Reports, 2, 336. https://doi.org/10.1038/srep00336. (PMID: 10.1038/srep00336224682233313482)
Lauritzen, S. L. (1996). Graphical models. Oxford: Clarendon Press. (PMID: 10.1093/oso/9780198522195.001.0001)
Massara, G. P., Di Matteo, T., & Aste, T. (2016). Network filtering for big data: Triangulated Maximally Filtered Graph. Journal of Complex Networks, 5, 161–178. https://doi.org/10.1093/comnet/cnw015. (PMID: 10.1093/comnet/cnw015)
Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74(3), 036104. https://doi.org/10.1103/PhysRevE.74.036104. (PMID: 10.1103/PhysRevE.74.036104)
Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103, 8577–8582. https://doi.org/10.1073/pnas.0601602103. (PMID: 10.1073/pnas.0601602103)
Plantié, M., & Crampes, M. (2012). Survey on social community detection. In N. Ramzan, R. van Zwol, J. S. Lee, K. Cluver, & X. S. Hua (Eds.), Social media retrieval (pp. 65–85). London, UK: Springer-Verlag. https://doi.org/10.1007/978-1-4471-4555-4_4.
Pons, P., & Latapy, M. (2006). Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications, 10, 191–218. https://doi.org/10.7155/jgaa.00185.
R Core Team. (2022). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/.
Raghavan, U. N., Albert, R., & Kumara, S. (2007). Near linear time algorithm to detect community structures in large-scale networks. Physical Review E, 76, 036106. https://doi.org/10.1103/PhysRevE.76.036106. (PMID: 10.1103/PhysRevE.76.036106)
Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. Physical Review E, 74, 016110. https://doi.org/10.1103/PhysRevE.74.016110. (PMID: 10.1103/PhysRevE.74.016110)
Revelle, W. (2017). psych: Procedures for psychological, psychometric, and personality research. Evanston, Illinois: Northwestern University. Retrieved from https://CRAN.R-project.org/package=psych.
Rosvall, M., & Bergstrom, C. T. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105, 1118–1123. https://doi.org/10.1073/pnas.0706851105. (PMID: 10.1073/pnas.0706851105)
Slocum-Gori, S. L., & Zumbo, B. D. (2011). Assessing the unidimensionality of psychological scales: Using multiple criteria from factor analysis. Social Indicators Research, 102, 443–461. https://doi.org/10.1007/s11205-010-9682-8. (PMID: 10.1007/s11205-010-9682-8)
Steinley, D., Brusco, M. J., & Hubert, L. (2016). The variance of the adjusted Rand index. Psychological Methods, 21, 261–272. https://doi.org/10.1037/met0000049. (PMID: 10.1037/met000004926881693)
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.
Ward, J. H. (1963). Hierarchical clustering to optimise an objective function. Journal of the American Statistical Association, 58, 238–244. (PMID: 10.1080/01621459.1963.10500845)
Williams, D. R. (2019). GGMnonreg: Estimate non-regularized Gaussian graphical models. Retrieved from https://github.com/donaldRwilliams/GGMnonreg.
Williams, D. R. (2021). Bayesian estimation for gaussian graphical models: Structure learning, predictability, and network comparisons. Multivariate Behavioral Research, 56(2), 336–352. https://doi.org/10.1080/00273171.2021.1894412. (PMID: 10.1080/00273171.2021.189441233739907)
Williams, D. R., & Mulder, J. (2020). BGGM: Bayesian Gaussian graphical models in R. Journal of Open Source Software, 5(51), 2111. https://doi.org/10.21105/joss.02111.
Williams, D. R., & Rast, P. (2018). Back to the basics: Rethinking partial correlation network methodology. Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/bmsp.12173.
Williams, D. R., Rhemtulla, M., Wysocki, A. C., & Rast, P. (2019). On nonregularized estimation of psychological networks. Multivariate Behavioral Research, 54, 719–750. https://doi.org/10.1080/00273171.2019.1575716. (PMID: 10.1080/00273171.2019.1575716309576296736701)
Wysocki, A. C., & Rhemtulla, M. (2019). On penalty parameter selection for estimating network models. Multivariate Behavioral Research, 1–15. https://doi.org/10.1080/00273171.2019.1672516.
Yang, Z., Algesheimer, R., & Tessone, C. J. (2016). A comparative analysis of community detection algorithms on artificial networks. Scientific Reports, 6, 30750. https://doi.org/10.1038/srep30750. (PMID: 10.1038/srep30750274764704967864)
Brusco, M. J., Steinley, D., & Watts, A. L. (2022). On maximization of the modularity index in network psychometrics. Behavior Research Methods, 1–17. https://doi.org/10.3758/s13428-022-01975-5.
Brusco, M., Steinley, D., & Watts, A. L. (2021). Spectral clustering of psychological networks. https://doi.org/10.31234/osf.io/fjqd4.
Brusco, M., Steinley, D., & Watts, A. L. (2022). A comparison of spectral clustering and the walktrap algorithm for community detection in network psychometrics. Psychological Methods. https://doi.org/10.1037/met0000509. (PMID: 10.1037/met000050935797161)
Castro, D., Ferreira, F., & Ferreira, T. B. (2020). Modularity of the personality network. European Journal of Psychological Assessment, 36, 998–1008. https://doi.org/10.1027/1015-5759/a000613. (PMID: 10.1027/1015-5759/a000613)
Cattell, R. B. (1978). The scientific use of factor analysis in behavioral and life sciences. Boston: Springer. https://doi.org/10.1007/978-1-4684-2262-7. (PMID: 10.1007/978-1-4684-2262-7)
Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., ... Borges, B. (2022). shiny: Web Application Framework for R. Retrieved from https://CRAN.R-project.org/package=shiny.
Chen, J., & Chen, Z. (2008). Extended bayesian information criteria for model selection with large model spaces. Biometrika, 95, 759–771. https://doi.org/10.1093/biomet/asn034. (PMID: 10.1093/biomet/asn034)
Christensen, A. P., & Golino, H. (2021). Estimating the stability of psychological dimensions via bootstrap exploratory graph analysis: A Monte Carlo simulation and tutorial. Psych, 3(3), 479–500. https://doi.org/10.3390/psych3030032. (PMID: 10.3390/psych3030032)
Clauset, A., Newman, M. E. J., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70, 066111. https://doi.org/10.1103/PhysRevE.70.066111. (PMID: 10.1103/PhysRevE.70.066111)
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. https://doi.org/10.1037/0033-2909.112.1.155. (PMID: 10.1037/0033-2909.112.1.15519565683)
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695, 1–9. Retrieved from https://www.semanticscholar.org/paper/The-igraph-software-package-for-complex-network-Cs/%C3/%A1rdi-Nepusz/1d2744b83519657f5f2610698a8ddd177ced4f5c?p2df.
De Beurs, D., Fried, E. I., Wetherall, K., Cleare, S., O’Connor, D. B., Ferguson, E., ... O’Connor, R. C. (2019). Exploring the psychology of suicidal ideation: A theory driven network analysis. Behaviour Research and Therapy, 120, 103419. https://doi.org/10.1016/j.brat.2019.103419.
Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48, 1–18. https://doi.org/10.18637/jss.v048.i04.
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23, 617–634. https://doi.org/10.1037/met0000167. (PMID: 10.1037/met000016729595293)
Epskamp, S., Kruis, J., & Marsman, M. (2017). Estimating psychopathological networks: Be careful what you wish for. PLoS ONE, 12(6). https://doi.org/10.1371/journal.pone.0179891.
Epskamp, S., Maris, G., Waldrop, L. J., & Borsboom, D. (2018). Network psychometrics. In P. Irwing, D. Hughes, & T. Booth (Eds.), The Wiley handbook of psychometric testing, 2 volume set: A multidisciplinary reference on survey, scale and test development. New York, NY: Wiley. https://doi.org/10.1002/9781118489772.ch30.
Fan, Y., Li, M., Zhang, P., Wu, J., & Di, Z. (2007). Accuracy and precision of methods for community identification in weighted networks. Physica A: Statistical Mechanics and Its Applications, 377, 363–372. https://doi.org/10.1016/j.physa.2006.11.036. (PMID: 10.1016/j.physa.2006.11.036)
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486, 75–174. https://doi.org/10.1016/j.physrep.2009.11.002. (PMID: 10.1016/j.physrep.2009.11.002)
Fortunato, S., & Barthelemy, M. (2007). Resolution limit in community detection. Proceedings of the National Academy of Sciences, 104(1), 36–41. https://doi.org/10.1073/pnas.0605965104. (PMID: 10.1073/pnas.0605965104)
Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models. In J. D. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, & A. Culotta (Eds.), Advances in neural information processing systems (pp. 604–612). Retrieved from https://papers.nips.cc/paper/4087-extended-bayesian-information-criteria-for-gaussian-graphical-models.
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 35–41. Retrieved from https://www.jstor.org/stable/3033543.
Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9, 432–441. https://doi.org/10.1093/biostatistics/kxm045. (PMID: 10.1093/biostatistics/kxm04518079126)
Friedman, J., Hastie, T., & Tibshirani, R. (2014). glasso: Graphical lasso – estimation of Gaussian graphical models. Retrieved from https://CRAN.R-project.org/package=glasso.
Garrido, L. E., Abad, F. J., & Ponsoda, V. (2011). Performance of Velicer’s minimum average partial factor retention method with categorical variables. Educational and Psychological Measurement, 71, 551–570. https://doi.org/10.1177/0013164410389489. (PMID: 10.1177/0013164410389489)
Garrido, L. E., Abad, F. J., & Ponsoda, V. (2013). A new look at Horn’s parallel analysis with ordinal variables. Psychological Methods, 18, 454–474. https://doi.org/10.1037/a0030005. (PMID: 10.1037/a003000523046000)
Gates, K. M., Henry, T., Steinley, D., & Fair, D. A. (2016). A Monte Carlo evaluation of weighted community detection algorithms. Frontiers in Neuroinformatics, 10, 45. https://doi.org/10.3389/fninf.2016.00045. (PMID: 10.3389/fninf.2016.00045278910875102890)
Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99, 7821–7826. https://doi.org/10.1073/pnas.122653799. (PMID: 10.1073/pnas.122653799)
Golino, H., & Epskamp, S. (2017). Exploratory Graph Analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE, 12, e0174035. https://doi.org/10.1371/journal.pone.0174035. (PMID: 10.1371/journal.pone.0174035285948395465941)
Golino, H., Moulder, R., Shi, D., Christensen, A. P., Neito, M. D., Nesselroade, J. R., Boker, & S. M. (2020). Entropy Fit Index: A new fit measure for assessing the structure and dimensionality of multiple latent variables. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2020.1779642.
Golino, H., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Sadana, R., ... Martinez-Molina, A. (2020). Investigating the performance of Exploratory Graph Analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychological Methods, 25, 292–320. https://doi.org/10.1037/met0000255.
Guttman, L. (1953). Image theory for the structure of quantitative variates. Psychometrika, 18, 277–296. (PMID: 10.1007/BF02289264)
Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66, 393–416. https://doi.org/10.1177/0013164405282485. (PMID: 10.1177/0013164405282485)
Hoffman, M., Steinley, D., Gates, K. M., Prinstein, M. J., & Brusco, M. J. (2018). Detecting clusters/communities in social networks. Multivariate Behavioral Research, 53(1), 57–73. https://doi.org/10.1080/00273171.2017.1391682. (PMID: 10.1080/00273171.2017.139168229220584)
Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185. https://doi.org/10.1007/BF02289447. (PMID: 10.1007/BF0228944714306381)
Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193–218. https://doi.org/10.1007/BF01908075. (PMID: 10.1007/BF01908075)
Isvoranu, A.-M., & Epskamp, S. (2021). Which estimation method to choose in network psychometrics? Deriving guidelines for applied researchers. Psychological Methods https://doi.org/10.1037/met0000439.
Jiménez, M., Abad, F. J., Garcia-Garzon, E., Golino, H., Christensen, A. P., & Garrido, L. E. (2022). Dimensionality assessment in generalized bi-factor structures: A network psychometrics approach. PsyArXiv. https://doi.org/10.31234/osf.io/2ujdk.
Kotov, R., Krueger, R. F., Watson, D., Achenbach, T. M., Althoff, R. R., Bagby, R. M., ... Zimmerman, M. (2017). The hierarchical taxonomy of psychopathology (HiTOP): A dimensional alternative to traditional nosologies. Journal of Abnormal Psychology, 126, 454–477. https://doi.org/10.1037/abn0000258.
Lancichinetti, A., & Fortunato, S. (2009). Community detection algorithms: A comparative analysis. Physical Review E, 80, 056117. https://doi.org/10.1103/PhysRevE.80.056117. (PMID: 10.1103/PhysRevE.80.056117)
Lancichinetti, A., & Fortunato, S. (2012). Consensus clustering in complex networks. Scientific Reports, 2, 336. https://doi.org/10.1038/srep00336. (PMID: 10.1038/srep00336224682233313482)
Lauritzen, S. L. (1996). Graphical models. Oxford: Clarendon Press. (PMID: 10.1093/oso/9780198522195.001.0001)
Massara, G. P., Di Matteo, T., & Aste, T. (2016). Network filtering for big data: Triangulated Maximally Filtered Graph. Journal of Complex Networks, 5, 161–178. https://doi.org/10.1093/comnet/cnw015. (PMID: 10.1093/comnet/cnw015)
Newman, M. E. J. (2006). Finding community structure in networks using the eigenvectors of matrices. Physical Review E, 74(3), 036104. https://doi.org/10.1103/PhysRevE.74.036104. (PMID: 10.1103/PhysRevE.74.036104)
Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103, 8577–8582. https://doi.org/10.1073/pnas.0601602103. (PMID: 10.1073/pnas.0601602103)
Plantié, M., & Crampes, M. (2012). Survey on social community detection. In N. Ramzan, R. van Zwol, J. S. Lee, K. Cluver, & X. S. Hua (Eds.), Social media retrieval (pp. 65–85). London, UK: Springer-Verlag. https://doi.org/10.1007/978-1-4471-4555-4_4.
Pons, P., & Latapy, M. (2006). Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications, 10, 191–218. https://doi.org/10.7155/jgaa.00185.
R Core Team. (2022). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/.
Raghavan, U. N., Albert, R., & Kumara, S. (2007). Near linear time algorithm to detect community structures in large-scale networks. Physical Review E, 76, 036106. https://doi.org/10.1103/PhysRevE.76.036106. (PMID: 10.1103/PhysRevE.76.036106)
Reichardt, J., & Bornholdt, S. (2006). Statistical mechanics of community detection. Physical Review E, 74, 016110. https://doi.org/10.1103/PhysRevE.74.016110. (PMID: 10.1103/PhysRevE.74.016110)
Revelle, W. (2017). psych: Procedures for psychological, psychometric, and personality research. Evanston, Illinois: Northwestern University. Retrieved from https://CRAN.R-project.org/package=psych.
Rosvall, M., & Bergstrom, C. T. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105, 1118–1123. https://doi.org/10.1073/pnas.0706851105. (PMID: 10.1073/pnas.0706851105)
Slocum-Gori, S. L., & Zumbo, B. D. (2011). Assessing the unidimensionality of psychological scales: Using multiple criteria from factor analysis. Social Indicators Research, 102, 443–461. https://doi.org/10.1007/s11205-010-9682-8. (PMID: 10.1007/s11205-010-9682-8)
Steinley, D., Brusco, M. J., & Hubert, L. (2016). The variance of the adjusted Rand index. Psychological Methods, 21, 261–272. https://doi.org/10.1037/met0000049. (PMID: 10.1037/met000004926881693)
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.
Ward, J. H. (1963). Hierarchical clustering to optimise an objective function. Journal of the American Statistical Association, 58, 238–244. (PMID: 10.1080/01621459.1963.10500845)
Williams, D. R. (2019). GGMnonreg: Estimate non-regularized Gaussian graphical models. Retrieved from https://github.com/donaldRwilliams/GGMnonreg.
Williams, D. R. (2021). Bayesian estimation for gaussian graphical models: Structure learning, predictability, and network comparisons. Multivariate Behavioral Research, 56(2), 336–352. https://doi.org/10.1080/00273171.2021.1894412. (PMID: 10.1080/00273171.2021.189441233739907)
Williams, D. R., & Mulder, J. (2020). BGGM: Bayesian Gaussian graphical models in R. Journal of Open Source Software, 5(51), 2111. https://doi.org/10.21105/joss.02111.
Williams, D. R., & Rast, P. (2018). Back to the basics: Rethinking partial correlation network methodology. Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/bmsp.12173.
Williams, D. R., Rhemtulla, M., Wysocki, A. C., & Rast, P. (2019). On nonregularized estimation of psychological networks. Multivariate Behavioral Research, 54, 719–750. https://doi.org/10.1080/00273171.2019.1575716. (PMID: 10.1080/00273171.2019.1575716309576296736701)
Wysocki, A. C., & Rhemtulla, M. (2019). On penalty parameter selection for estimating network models. Multivariate Behavioral Research, 1–15. https://doi.org/10.1080/00273171.2019.1672516.
Yang, Z., Algesheimer, R., & Tessone, C. J. (2016). A comparative analysis of community detection algorithms on artificial networks. Scientific Reports, 6, 30750. https://doi.org/10.1038/srep30750. (PMID: 10.1038/srep30750274764704967864)
Grant Information:
2018-2019-1D2-085 Fondo Nacional de Innovación y Desarrollo Científico y Tecnológico (FONDOCYT) of the Dominican Republic
Contributed Indexing:
Keywords: Community detection; Dimension reduction; Network psychometrics
Entry Date(s):
Date Created: 20230616 Date Completed: 20240405 Latest Revision: 20240906
Update Code:
20260130
DOI:
10.3758/s13428-023-02106-4
PMID:
37326769
Database:
MEDLINE
*Further Information*
*Identifying the correct number of factors in multivariate data is fundamental to psychological measurement. Factor analysis has a long tradition in the field, but it has been challenged recently by exploratory graph analysis (EGA), an approach based on network psychometrics. EGA first estimates a network and then applies the Walktrap community detection algorithm. Simulation studies have demonstrated that EGA has comparable or better accuracy for recovering the same number of communities as there are factors in the simulated data than factor analytic methods. Despite EGA's effectiveness, there has yet to be an investigation into whether other sparsity induction methods or community detection algorithms could achieve equivalent or better performance. Furthermore, unidimensional structures are fundamental to psychological measurement yet they have been sparsely studied in simulations using community detection algorithms. In the present study, we performed a Monte Carlo simulation using the zero-order correlation matrix, GLASSO, and two variants of a non-regularized partial correlation sparsity induction methods with several community detection algorithms. We examined the performance of these method-algorithm combinations in both continuous and polytomous data across a variety of conditions. 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 overall.
(© 2023. The Psychonomic Society, Inc.)*