* [45] S. Epskamp, L. J. Waldorp, R. Mõttus, and D. Borsboom.  The Gaussian graphical model in cross-sectional and time-series data.  Multivar. Behav. Res., 53:453–480, 2018.
  * [46] W. Lutz, B. Schwartz, S. G. Hofmann, A. J. Fisher, K. Husen, and J. A. Rubel.  Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: A methodological proof-of-concept study.  Sci. Rep., 8:7819, 2018.
  * [47] D. C. R. van Zelst, E. M. Veltman, D. Rhebergen, P. Naarding, A. A. L. Kok, N. R. Ottenheim, and E. J. Giltay.  Network structure of time-varying depressive symptoms through dynamic time warp analysis in late-life depression.  Int. J. Geriatr. Psychiatry, 37:1–12, 2022.
  * [48] M. K. Forbes, A. G. C. Wright, K. E. Markon, and R. F. Krueger.  Evidence that psychopathology symptom networks have limited replicability.  J. Abnormal Psychol., 126:969–988, 2017.
  * [49] E. Bullmore and O. Sporns.  Complex brain networks: Graph theoretical analysis of structural and functional systems.  Nat. Rev. Neurosci., 10:186–198, 2009.
  * [50] D. S. Bassett and O. Sporns.  Network neuroscience.  Nat. Neurosci., 20:353–364, 2017.
  * [51] J. Chung, E. Bridgeford, J. Arroyo, B. D. Pedigo, A. Saad-Eldin, V. Gopalakrishnan, L. Xiang, C. E. Priebe, and J. T. Vogelstein.  Statistical connectomics.  Annu. Rev. Stat. Appl., 8:463–492, 2021.
  * [52] R. C. Craddock, S. Jbabdi, C.-G. Yan, J. T. Vogelstein, F. X. Castellanos, A. Di Martino, C. Kelly, K. Heberlein, S. Colcombe, and M. P. Milham.  Imaging human connectomes at the macroscale.  Nat. Methods, 10:524–539, 2013.
  * [53] B. Biswal, F. Z. Yetkin, V. M. Haughton, and J. S. Hyde.  Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.  Magnetic Resonance in Medicine, 34:537–541, 1995.
  * [54] G. L. Colclough, M. W. Woolrich, P. K. Tewarie, M. J. Brookes, A. J. Quinn, and S. M. Smith.  How reliable are MEG resting-state connectivity metrics?  NeuroImage, 138:284–293, 2016.
  * [55] A. Alexander-Bloch, J. N. Giedd, and E. Bullmore.  Imaging structural co-variance between human brain regions.  Nat. Rev. Neurosci., 14:322–336, 2013.
  * [56] A. C. Evans.  Networks of anatomical covariance.  NeuroImage, 80:489–504, 2013.
  * [57] J. Seidlitz, F. Váša, M. Shinn, R. Romero-Garcia, K. J. Whitaker, P. E. Vértes, K. Wagstyl, P. Kirkpatrick Reardon, L. Clasen, S. Liu, A. Messinger, D. A. Leopold, P. Fonagy, R. J. Dolan, P. B. Jones, I. M. Goodyer, the NSPN Consortium, A. Raznahan, and E. T. Bullmore.  Morphometric similarity networks detect microscale cortical organization and predict inter-individual cognitive variation.  Neuron, 97:231–247, 2018.
  * [58] J. Y. Hansen, G. Shafiei, R. D. Markello, K. Smart, S. M. L. Cox, M. Nørgaard, V. Beliveau, Y. Wu, J.-D. Gallezot, É. Aumont, S. Servaes, S. G. Scala, J. M. DuBois, G. Wainstein, G. Bezgin, T. Funck, T. W. Schmitz, R. N. Spreng, M. Galovic, M. J. Koepp, J. S. Duncan, J. P. Coles, T. D. Fryer, F. I. Aigbirhio, C. J. McGinnity, A. Hammers, J.-P. Soucy, S. Baillet, S. Guimond, J. Hietala, M.-A. Bedard, M. Leyton, E. Kobayashi, P. Rosa-Neto, M. Ganz, G. M. Knudsen, N. Palomero-Gallagher, J. M. Shine, R. E. Carson, L. Tuominen, A. Dagher, and B. Misic.  Mapping neurotransmitter systems to the structural and functional organization of the human neocortex.  Nat. Neurosci., 25:1569–1581, 2022.
  * [59] S. Horvath and J. Dong.  Geometric interpretation of gene coexpression network analysis.  PLoS Comput. Biol., 4:e1000117, 2008.
  * [60] P. Langfelder and S. Horvath.  WGCNA: An R package for weighted correlation network analysis.  BMC Bioinformatics, 9:559, 2008.
  * [61] B. H. Junker and F. Schreiber, editors.  Analysis of Biological Networks.  John Wiley & Sons, Inc., Hoboken, NJ, 2008.
  * [62] M. Vidal, M. E. Cusick, and A.-L. Barabási.  Interactome networks and human disease.  Cell, 144:986–998, 2011.
  * [63] C. Gaiteri, Y. Ding, B. French, G. C. Tseng, and E. Sibille.  Beyond modules and hubs: The potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders.  Genes Brain Behav., 13:13–24, 2014.
  * [64] G. Fiscon, F. Conte, L. Farina, and P. Paci.  Network-based approaches to explore complex biological systems towards network medicine.  Genes, 9:437, 2018.
  * [65] S. van Dam, U. Võsa, A. van der Graaf, L. Franke, and J. P. de Magalhães.  Gene co-expression analysis for functional classification and gene–disease predictions.  Brief. Bioinfo., 19:575–592, 2018.
  * [66] M. Mircea, M. Hochane, X. Fan, S. M. Chuva de Sousa Lopes, D. Garlaschelli, and S. Semrau.  Phiclust: A clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations.  Genome Biol., 23:18, 2022.
  * [67] K. S. Parker, J. D. Wilson, J. Marschall, P. J. Mucha, and J. P. Henderson.  Network analysis reveals sex- and antibiotic resistance-associated antivirulence targets in clinical uropathogens.  ACS Infectious Diseases, 1:523–532, 2015.
  * [68] Z. Zou, R. F. Potter, W. H. McCoy 4th, J. A. Wildenthal, G. L. Katumba, P. J. Mucha, G. Dantas, and J. P. Henderson.  E. coli catheter-associated urinary tract infections are associated with distinctive virulence and biofilm gene determinants.  JCI Insight, 8:e161461, 2023.
  * [69] W.-C. Chou, A.-L. Cheng, M. Brotto, and C.-Y. Chuang.  Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer.  BMC Genomics, 15:300, 2014.
  * [70] R. Dobrin, J. Zhu, C. Molony, C. Argman, M. L. Parrish, S. Carlson, M. F. Allan, D. Pomp, and E. E. Schadt.  Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease.  Genome Biol., 10:R55, 2009.
  * [71] Y. Xiang, J. Zhang, and K. Huang.  Mining the tissue-tissue gene co-expression network for tumor microenvironment study and biomarker prediction.  BMC Genomics, 14:S4, 2013.
  * [72] L. J. A. Kogelman, J. Fu, L. Franke, J. W. Greve, M. Hofker, S. S. Rensen, and H. N. Kadarmideen.  Inter-tissue gene co-expression networks between metabolically healthy and unhealthy obese individuals.  PLoS ONE, 11:e0167519, 2016.
  * [73] N. J. Hudson, A. Reverter, and B. P. Dalrymple.  A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.  PLoS Comput. Biol., 5:e1000382, 2009.
  * [74] R. Steuer.  On the analysis and interpretation of correlations in metabolomic data.  Brief. Bioinfo., 7:151–158, 2006.
  * [75] E. K. Silverman, H. H. H. W. Schmidt, E. Anastasiadou, L. Altucci, M. Angelini, L. Badimon, J.-L. Balligand, G. Benincasa, G. Capasso, F. Conte, A. Di Costanzo, L. Farina, G. Fiscon, L. Gatto, M. Gentili, J. Loscalzo, C. Marchese, C. Napoli, P. Paci, M. Petti, J. Quackenbush, P. Tieri, D. Viggiano, G. Vilahur, K. Glass, and J. Baumbach.  Molecular networks in Network Medicine: Development and applications.  Wiley Interdisciplinary Reviews: Syst. Biol. Med., 12:e1489, 2020\.
  * [76] D. Camacho, A. de la Fuente, and P. Mendes.  The origin of correlations in metabolomics data.  Metabolomics, 1:53–63, 2005.
  * [77] J. I. Robinson, W. H. Weir, J. R. Crowley, T. Hink, K. A. Reske, J. H. Kwon, C.-A. D. Burnham, E. R. Dubberke, P. J. Mucha, and J. P. Henderson.  Metabolomic networks connect host-microbiome processes to human Clostridioides difficile infections.  J. Clin. Invest., 129:3792–3806, 2019.
  * [78] H. Hirano and K. Takemoto.  Difficulty in inferring microbial community structure based on co-occurrence network approaches.  BMC Bioinformatics, 20:329, 2019.
  * [79] M. Goberna and M. Verdú.  Cautionary notes on the use of co-occurrence networks in soil ecology.  Soil Biol. Biochem., 166:108534, 2022.
  * [80] J. M. Diamond.  Assembly of species communities.  In M. L. Cody and J. M. Diamond, editors, Ecology and Evolution of Communities, pages 342–444. Harvard University Press, Cambridge, MA, 1975\.
  * [81] J. X. Hu, C. E. Thomas, and S. Brunak.  Network biology concepts in complex disease comorbidities.  Nat. Rev. Genet., 17:615–629, 2016.
  * [82] C. A. Hidalgo, N. Blumm, A.-L. Barabási, and N. A. Christakis.  A dynamic network approach for the study of human phenotypes.  PLoS Comput. Biol., 5:e1000353, 2009.
  * [83] K.-I. Goh, M. E. Cusick, D. Valle, B. Childs, M. Vidal, and A.-L. Barabási.  The human disease network.  Proc. Natl. Acad. Sci. USA, 104:8685–8690, 2007.
  * [84] A. Rzhetsky, D. Wajngurt, N. Park, and T. Zheng.  Probing genetic overlap among complex human phenotypes.  Proc. Natl. Acad. Sci. USA, 104:11694–11699, 2007.
  * [85] D.-S. Lee, J. Park, K. A. Kay, N. A. Christakis, Z. N. Oltvai, and A.-L. Barabási.  The implications of human metabolic network topology for disease comorbidity.  Proc. Natl. Acad. Sci. USA, 105:9880–9885, 2008.
  * [86] X. Zhou, J. Menche, A.-L. Barabási, and A. Sharma.  Human symptoms–disease network.  Nat. Commun., 5:4212, 2014.
  * [87] R. N. Mantegna and H. E. Stanley.  An Introduction to Econophysics.  Cambridge University Press, Cambridge, UK, 1999.
  * [88] L. Laloux, P. Cizeau, J.-P. Bouchaud, and M. Potters.  Noise dressing of financial correlation matrices.  Phys. Rev. Lett., 83:1467–1470, 1999.
  * [89] V. Plerou, P. Gopikrishnan, B. Rosenow, L. A. Nunes Amaral, and H. E. Stanley.  Universal and nonuniversal properties of cross correlations in financial time series.  Phys. Rev. Lett., 83:1471–1474, 1999.
  * [90] M. MacMahon and D. Garlaschelli.  Community detection for correlation matrices.  Phys. Rev. X, 5:021006, 2015.
  * [91] Almog, A. and Besamusca, F. and MacMahon, M. and Garlaschelli, D.  Mesoscopic community structure of financial markets revealed by price and sign fluctuations.  PLoS ONE, 10:e0133679, 2015.
  * [92] S. M. Zema, G. Fagiolo, T. Squartini, and D. Garlaschelli.  Mesoscopic structure of the stock market and portfolio optimization.  Preprint arXiv:2112.06544, 2021.
  * [93] I. Anagnostou, T. Squartini, D. Kandhai, and D. Garlaschelli.  Uncovering the mesoscale structure of the credit default swap market to improve portfolio risk modelling.  Quantitative Finance, 21:1501–1518, 2021.
  * [94] R. N. Mantegna.  Hierarchical structure in financial markets.  Eur. Phys. J. B, 11:193–197, 1999.
  * [95] M. Tumminello, F. Lillo, J. Piilo, and R. N. Mantegna.  Identification of clusters of investors from their real trading activity in a financial market.  New J. Phys., 14:013041, 2012.
  * [96] S. Ranganathan, M. Kivelä, and J. Kanniainen.  Dynamics of investor spanning trees around dot-com bubble.  PLoS ONE, 13:e0198807, 2018.
  * [97] D. Kercher.  Inconsistent correlation and momenta: A new approach to portfolio allocation.  MS Thesis. Brigham Young University, 2023.
  * [98] E. Yan and Y. Ding.  Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other.  J. Amer. Soc. Info. Sci. Tech., 63:1313–1326, 2012.
  * [99] J.-P. Qiu, K. Dong, and H.-Q. Yu.  Comparative study on structure and correlation among author co-occurrence networks in bibliometrics.  Scientometrics, 101:1345–1360, 2014.
  * [100] M. E. J. Newman.  Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality.  Phys. Rev. E, 64:016132, 2001.
  * [101] C. Cattuto, A. Barrat, A. Baldassarri, G. Schehr, and V. Loreto.  Collective dynamics of social annotation.  Proc. Natl. Acad. Sci. USA, 106:10511–10515, 2009.
  * [102] F. Luo, J. Z. Wang, and E. Promislow.  Exploring local community structures in large networks.  In Proceedings of 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI’06), pages 233–239, 2006.
  * [103] A. A. Tsonis and P. J. Roebber.  The architecture of the climate network.  Physica A, 333:497–504, 2004.
  * [104] A. A. Tsonis, K. L. Swanson, and P. J. Roebber.  What do networks have to do with climate?  Bull. Amer. Meteorol. Soc., 87:585–595, 2006.
  * [105] J. F. Donges, Y. Zou, N. Marwan, and J. Kurths.  The backbone of the climate network.  EPL, 87:48007, 2009.
  * [106] J. Fan, J. Meng, J. Ludescher, X. Chen, Y. Ashkenazy, J. Kurths, S. Havlin, and H. J. Schellnhuber.  Statistical physics approaches to the complex Earth system.  Phys. Rep., 896:1–84, 2021.
  * [107] J. Heitzig, J. F. Donges, Y. Zou, N. Marwan, and J. Kurths.  Node-weighted measures for complex networks with spatially embedded, sampled, or differently sized nodes.  Eur. Phys. J. B, 85:38, 2012.
  * [108] S. Scarsoglio, F. Laio, and L. Ridolfi.  Climate dynamics: A network-based approach for the analysis of global precipitation.  PLoS ONE, 8:e71129, 2013.
  * [109] M. van der Mheen, H. A. Dijkstra, A. Gozolchiani, M. den Toom, Q. Feng, J. Kurths, and E. Hernandez-Garcia.  Interaction network based early warning indicators for the Atlantic MOC collapse.  Geophys. Res. Lett., 40:2714–2719, 2013.
  * [110] P. J. Bickel and E. Levina.  Covariance regularization by thresholding.  Ann. Stat., 36:2577–2604, 2008.
  * [111] M. Pourahmadi.  Covariance estimation: The GLM and regularization perspectives.  Statist. Sci., 26:369–387, 2011.
  * [112] J. Fan, Y. Liao, and H. Liu.  An overview of the estimation of large covariance and precision matrices.  Econometrics J., 19:C1–C32, 2016.
  * [113] J. Whittaker.  Graphical Models in Applied Multivariate Statistics.  John Wiley & Sons, Chichester, UK, 1990.
  * [114] S. L. Lauritzen.  Graphical Models.  Clarendon Press, Oxford, UK, 1996.
  * [115] A. P. Dempster.  Covariance selection.  Biometrics, 28:157–175, 1972.
  * [116] O. Ledoit and M. Wolf.  The power of (non-)linear shrinking: A review and guide to covariance matrix estimation.  J. Financial Econometrics, 20:187–218, 2022.
  * [117] O. Ledoit and M. Wolf.  Improved estimation of the covariance matrix of stock returns with an application to portfolio selection.  J. Empirical Finance, 10:603–621, 2003.
  * [118] O. Ledoit and M. Wolf.  A well-conditioned estimator for large-dimensional covariance matrices.  J. Multivar. Anal., 88:365–411, 2004.
  * [119] Y. Chen, A. Wiesel, Y. C. Eldar, and A. O. Hero.  Shrinkage algorithms for MMSE covariance estimation.  IEEE Trans. Signal Proc., 58:5016–5029, 2010.
  * [120] R. B. Holmes.  On random correlation matrices.  SIAM J. Matrix Anal. Appl., 12:239–272, 1991.
  * [121] J. A. Tropp.  Simplicial faces of the set of correlation matrices.  Discrete Comput. Geom., 60:512–529, 2018.
  * [122] Y. Thanwerdas and X. Pennec.  Geodesics and curvature of the quotient-affine metrics on full-rank correlation matrices.  Lect. Notes Comput. Sci., 12829:93–102, 2021.
  * [123] M. Rubinov and O. Sporns.  Weight-conserving characterization of complex functional brain networks.  NeuroImage, 56:2068–2079, 2011.
  * [124] K. A. Garrison, D. Scheinost, E. S. Finn, X. Shen, and R. T. Constable.  The (in)stability of functional brain network measures across thresholds.  NeuroImage, 118:651–661, 2015.
  * [125] F. De Vico Fallani, V. Latora, and M. Chavez.  A topological criterion for filtering information in complex brain networks.  PLoS Comput. Biol., 13:e1005305, 2017.
  * [126] M. W. Cole, S. Pathak, and W. Schneider.  Identifying the brain’s most globally connected regions.  NeuroImage, 49:3132–3148, 2010.
  * [127] D. Scheinost, J. Benjamin, C. M. Lacadie, B. Vohr, K. C. Schneider, L. R. Ment, X. Papademetris, and R. T. Constable.  The intrinsic connectivity distribution: A novel contrast measure reflecting voxel level functional connectivity.  NeuroImage, 62:1510–1519, 2012.
  * [128] L. Peel, T. P. Peixoto, and M. De Domenico.  Statistical inference links data and theory in network science.  Nat. Commun., 13:6794, 2022.
  * [129] M. P. van den Heuvel, S. C. de Lange, A. Zalesky, C. Seguin, B. T. T. Yeo, and R. Schmidt.  Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations.  NeuroImage, 152:437–449, 2017.
  * [130] S. Achard and E. Bullmore.  Efficiency and cost of economical brain functional networks.  PLoS Comput. Biol., 3:e17, 2007.
  * [131] M. P. van den Heuvel, C. J. Stam, M. Boersma, and H. E. Hulshoff Pol.  Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain.  NeuroImage, 43:528–539, 2008.
  * [132] J. Wang, L. Wang, Y. Zang, H. Yang, H. Tang, Q. Gong, Z. Chen, C. Zhu, and Y. He.  Parcellation-dependent small-world brain functional networks: A resting-state fMRI study.  Human Brain Mapping, 30:1511–1523, 2009.
  * [133] B. C. M. van Wijk, C. J. Stam, and A. Daffertshofer.  Comparing brain networks of different size and connectivity density using graph theory.  PLoS ONE, 5:e13701, 2010.
  * [134] A. F. Alexander-Bloch, N. Gogtay, D. Meunier, R. Birn, L. Clasen, F. Lalonde, R. Lenroot, J. Giedd, and E. T. Bullmore.  Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia.  Front. Syst. Neurosci., 4:147, 2010.
  * [135] E. Langford, N. Schwertman, and M. Owens.  Is the property of being positively correlated transitive?  Am. Stat., 55:322–325, 2001.
  * [136] J. Gillis and P. Pavlidis.  The role of indirect connections in gene networks in predicting function.  Bioinformatics, 27:1860–1866, 2011.
  * [137] A. Zalesky, A. Fornito, and E. Bullmore.  On the use of correlation as a measure of network connectivity.  NeuroImage, 60:2096–2106, 2012.
  * [138] M. Barthélemy.  Spatial networks.  Phys. Rep., 499:1–101, 2011.
  * [139] M. Barthelemy.  Morphogenesis of Spatial Networks.  Springer, Cham, Switzerland, 2018.
  * [140] N. Langer, A. Pedroni, and L. Jäncke.  The problem of thresholding in small-world network analysis.  PLoS ONE, 8:e53199, 2013.
  * [141] M.-E. Lynall, D. S. Bassett, R. Kerwin, P. J. McKenna, M. Kitzbichler, U. Muller, and E. Bullmore.  Functional connectivity and brain networks in schizophrenia.  J. Neurosci., 30:9477–9487, 2010.
  * [142] C. E. Ginestet, T. E. Nichols, E. T. Bullmore, and A. Simmons.  Brain network analysis: Separating cost from topology using cost-integration.  PLoS ONE, 6:e21570, 2011.
  * [143] D. S. Bassett, B. G. Nelson, B. A. Mueller, J. Camchong, and K. O. Lim.  Altered resting state complexity in schizophrenia.  NeuroImage, 59:2196–2207, 2012.
  * [144] S. M. H. Hosseini, F. Hoeft, and S. R. Kesler.  GAT: A graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.  PLoS ONE, 7:e40709, 2012.
  * [145] V. Latora and M. Marchiori.  Efficient behavior of small-world networks.  Phys. Rev. Lett., 87:198701, 2001.
  * [146] D. S. Bassett, A. Meyer-Lindenberg, S. Achard, T. Duke, and E. Bullmore.  Adaptive reconfiguration of fractal small-world human brain functional networks.  Proc. Natl. Acad. Sci. USA, 103:19518–19523, 2006.
  * [147] N. Vandewalle, F. Brisbois, and X. Tordoir.  Non-random topology of stock markets.  Quant. Finance, 1:372–374, 2001.
  * [148] J.-P. Onnela, A. Chakraborti, K. Kaski, and J. Kertész.  Dynamic asset trees and portfolio analysis.  Eur. Phys. J. B, 30:285–288, 2002.
  * [149] M. Tumminello, T. Aste, T. Di Matteo, and R. N. Mantegna.  A tool for filtering information in complex systems.  Proc. Natl. Acad. Sci. USA, 102:10421–10426, 2005.
  * [150] M. Á Serrano, M. Boguñá, and A. Vespignani.  Extracting the multiscale backbone of complex weighted networks.  Proc. Natl. Acad. Sci. USA, 106:6483–6488, 2009.
  * [151] S. Horvath.  Weighted Network Analysis.  Springer, New York, NY, 2011.
  * [152] D. L. Donoho and I. M. Johnstone.  Ideal spatial adaptation by wavelet shrinkage.  Biometrika, 81:425–455, 1994.
  * [153] N. El Karoui.  Operator norm consistent estimation of large-dimensional sparse covariance matrices.  Ann. Stat., 36:2717–2756, 2008.
  * [154] A. J. Rothman, E. Levina, and J. Zhu.  Generalized thresholding of large covariance matrices.  J. Am. Stat. Assoc., 104:177–186, 2009.
  * [155] R. Tibshirani.  Regression shrinkage and selection via the lasso.  J. R. Statist. Soc. B, 58:267–288, 1996.
  * [156] T. Cai and W. Liu.  Adaptive thresholding for sparse covariance matrix estimation.  J. Am. Stat. Assoc., 106:672–684, 2011.
  * [157] K. R. A. Van Dijk, T. Hedden, A. Venkataraman, K. C. Evans, S. W. Lazar, and R. L. Buckner.  Intrinsic functional connectivity as a tool for human connectomics: Theory, properties, and optimization.  J. Neurophysiol., 103:297–321, 2010.
  * [158] J. Tang, Y. Chang, C. Aggarwal, and H. Liu.  A survey of signed network mining in social media.  ACM Comput. Surv., 49:42, 2016.
  * [159] L. Zhan, L. M. Jenkins, O. E. Wolfson, J. J. GadElkarim, K. Nocito, P. M. Thompson, O. A. Ajilore, M. K. Chung, and A. D. Leow.  The significance of negative correlations in brain connectivity.  J. Comparative Neurol., 525:3251–3265, 2017.
  * [160] S. Gómez, P. Jensen, and A. Arenas.  Analysis of community structure in networks of correlated data.  Phys. Rev. E, 80:016114, 2009.
  * [161] C. Aicher, A. Z. Jacobs, and A. Clauset.  Learning latent block structure in weighted networks.  J. Comp. Netw., 3:221–248, 2015.
  * [162] T. P. Peixoto.  Nonparametric weighted stochastic block models.  Phys. Rev. E, 97:012306, 2018.
  * [163] A. de la Fuente, N. Bing, I. Hoeschele, and P. Mendes.  Discovery of meaningful associations in genomic data using partial correlation coefficients.  Bioinformatics, 20:3565–3574, 2004.
  * [164] R. Salvador, J. Suckling, M. R. Coleman, J. D. Pickard, D. Menon, and E. Bullmore.  Neurophysiological architecture of functional magnetic resonance images of human brain.  Cereb. Cortex, 15:1332–1342, 2005.
  * [165] G. Marrelec, A. Krainik, H. Duffau, M. Pélégrini-Issac, S. Lehéricy, J. Doyon, and H. Benali.  Partial correlation for functional brain interactivity investigation in functional MRI.  NeuroImage, 32:228–237, 2006.
  * [166] M. Goldstein and A. F. M. Smith.  Ridge-type estimators for regression analysis.  J. R. Statist. Soc. Ser. B, 36:284–291, 1974.
  * [167] A. Hero and B. Rajaratnam.  Hub discovery in partial correlation graphs.  IEEE Trans. Info. Th., 58:6064–6078, 2012.
  * [168] R. Artner, P. P. Wellingerhof, G. Lafit, T. Loossens, W. Vanpaemel, and F. Tuerlinckx.  The shape of partial correlation matrices.  Commun. Stat., 51:4133–4150, 2022.
  * [169] T. Aste and T. Di Matteo.  Dynamical networks from correlations.  Physica A, 370:156–161, 2006.
  * [170] D. R. Williams.  Learning to live with sampling variability: Expected replicability in partial correlation networks.  Psychol. Methods, 27:606–621, 2022.
  * [171] T. Millington and M. Niranjan.  Partial correlation financial networks.  Appl. Netw. Sci., 5:11, 2020.
  * [172] N. Meinshausen and P. Bühlmann.  High-dimensional graphs and variable selection with the Lasso.  Ann. Stat., 34:1436–1462, 2006.
  * [173] M. Yuan and Y. Lin.  Model selection and estimation in the Gaussian graphical model.  Biometrika, 94:19–35, 2007.
  * [174] O. Banerjee, L. El Ghaoui, and A. d’Aspremont.  Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data.  J. Machine Learning Res., 9:485–516, 2008.
  * [175] J. Friedman, T. Hastie, and R. Tibshirani.  Sparse inverse covariance estimation with the graphical lasso.  Biostatistics, 9:432–441, 2008.
  * [176] J. Fan, Y. Feng, and Y. Wu.  Network exploration via the adaptive LASSO and SCAD penalties.  Ann. Appl. Stat., 3:521–541, 2009.
  * [177] R. Foygel and M. Drton.  Extended Bayesian information criteria for Gaussian graphical models.  Advances in Neural Information Processing Systems 23, 23:604–612, 2010.
  * [178] T. Hastie, R. Tibshirani, and M. Wainwright.  Statistical Learning with Sparsity: The Lasso and Generalizations.  CRC Press, Boca Raton, FL, 2015.
  * [179] J. Schäfer and K. Strimmer.  An empirical Bayes approach to inferring large-scale gene association networks.  Bioinformatics, 21:754–764, 2005.
  * [180] H. Li and J. Gui.  Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks.  Biostatistics, 7:302–317, 2006.
  * [181] A. d’Aspremont, O. Banerjee, and L. El Ghaoui.  First-order methods for sparse covariance selection.  SIAM J. Matrix Anal. Appl., 30:56–66, 2008.
  * [182] J. Z. Huang, N. Liu, M. Pourahmadi, and L. Liu.  Covariance matrix selection and estimation via penalised normal likelihood.  Biometrika, 93:85–98, 2006.
  * [183] J. Bien and R. J. Tibshirani.  Sparse estimation of a covariance matrix.  Biometrika, 98:807–820, 2011.
  * [184] H. Wang.  Coordinate descent algorithm for covariance graphical lasso.  Stat. Comput., 24:521–529, 2014.
  * [185] H. Wang.  Scaling it up: Stochastic search structure learning in graphical models.  Bayesian Analysis, 10:351–377, 2015.
  * [186] S. Kojaku and N. Masuda.  Constructing networks by filtering correlation matrices: A null model approach.  Proc. R Soc. A, 475:20190578, 2019.
  * [187] N. Masuda and R. Lambiotte.  A Guide to Temporal Networks.  World Scientific, Singapore, second edition, 2020.
  * [188] P. Holme and J. Saramäki.  Temporal networks.  Phys. Rep., 519:97–125, 2012.
  * [189] P. Holme.  Modern temporal network theory: A colloquium.  Eur. Phys. J. B, 88:234, 2015.
  * [190] M. Bazzi, M. A. Porter, S. Williams, M. McDonald, D. J. Fenn, and S. D. Howison.  Community detection in temporal multilayer networks, with an application to correlation networks.  Multiscale Model. Simul., 1:1–41, 2016.
  * [191] R. Hindriks, M. H. Adhikari, Y. Murayama, M. Ganzetti, D. Mantini, N. K. Logothetis, and G. Deco.  Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?  NeuroImage, 127:242–256, 2016.
  * [192] Z. Adams, R. Füss, and T. Glück.  Are correlations constant? Empirical and theoretical results on popular correlation models in finance.  J. Banking Finance, 84:9–24, 2017.
  * [193] M. A. Lindquist, Y. Xu, M. B. Nebel, and B. S. Caffo.  Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach.  NeuroImage, 101:531–546, 2014.
  * [194] J.-P. Onnela, A. Chakraborti, K. Kaski, J. Kertész, and A. Kanto.  Dynamics of market correlations: Taxonomy and portfolio analysis.  Phys. Rev. E, 68:056110, 2003.
  * [195] J.-P. Onnela, K. Kaski, and J. Kertész.  Clustering and information in correlation based financial networks.  Eur. Phys. J. B, 38:353–362, 2004.
  * [196] R. M. Hutchison, T. Womelsdorf, E. A. Allen, P. A. Bandettini, V. D. Calhoun, M. Corbetta, S. Della Penna, J. H. Duyn, G. H. Glover, J. Gonzalez-Castillo, D. A. Handwerker, S. Keilholz, V. Kiviniemi, D. A. Leopold, F. de Pasquale, O. Sporns, M. Walter, and C. Chang.  Dynamic functional connectivity: Promise, issues, and interpretations.  NeuroImage, 80:360–378, 2013.
  * [197] V. D. Calhoun, R. Miller, G. Pearlson, and T. Adalı.  The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery.  Neuron, 84:262–274, 2014.
  * [198] M. Filippi, E. G. Spinelli, C. Cividini, and F. Agosta.  Resting state dynamic functional connectivity in neurodegenerative conditions: A review of magnetic resonance imaging findings.  Front. Neurosci., 13:657, 2019.
  * [199] D. J. Lurie, D. Kessler, D. S. Bassett, R. F. Betzel, M. Breakspear, S. Kheilholz, A. Kucyi, R. Liégeois, M. A. Lindquist, A. R. McIntosh, R. A. Poldrack, J. M. Shine, W. H. Thompson, N. Z. Bielczyk, L. Douw, D. Kraft, R. L. Miller, M. Muthuraman, L. Pasquini, A. Razi, D. Vidaurre, H. Xie, and V. D. Calhoun.  Questions and controversies in the study of time-varying functional connectivity in resting fMRI.  Netw. Neurosci., 4:30–69, 2020.
  * [200] D. S. Bassett, N. F. Wymbs, M. A. Porter, P. J. Mucha, J. M. Carlson, and S. T. Grafton.  Dynamic reconfiguration of human brain networks during learning.  Proc. Natl. Acad. Sci. USA, 108:7641–7646, 2011.
  * [201] D. S. Bassett, N. F. Wymbs, M. P. Rombach, M. A. Porter, P. J. Mucha, and S. T. Grafton.  Task-based core-periphery organization of human brain dynamics.  PLoS Comput. Biol., 9:e1003171, 2013.
  * [202] U. Braun, A. Schäfer, H. Walter, S. Erk, N. Romanczuk-Seiferth, L. Haddad, J. I. Schweiger, O. Grimm, A. Heinz, H. Tost, A. Meyer-Lindenberg, and D. S. Bassett.  Dynamic reconfiguration of frontal brain networks during executive cognition in humans.  Proc. Natl. Acad. Sci. USA, 112:11678–11683, 2015.
  * [203] J. Nakajima and M. West.  Bayesian analysis of latent threshold dynamic models.  J. Busi. Econ. Stat., 31:151–164, 2013.
  * [204] J. Nakajima and M. West.  Dynamic network signal processing using latent threshold models.  Digital Signal Proc., 47:5–16, 2015.
  * [205] D. Hallac, Y. Park, S. Boyd, and J. Leskovec.  Network inference via the time-varying graphical lasso.  In Proceedings of the 23th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 205–213, 2017.
  * [206] D. J. Watts and S. H. Strogatz.  Collective dynamics of ‘small-world’ networks.  Nature, 393:440–442, 1998.
  * [207] B. K. Fosdick, D. B. Larremore, J. Nishimura, and J. Ugander.  Configuring random graph models with fixed degree sequences.  SIAM Review, 60:315–355, 2018.
  * [208] F. Váša and B. Mišić.  Null models in network neuroscience.  Nat. Rev. Neurosci., 23:493–504, 2022.
  * [209] R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon.  Network motifs: simple building blocks of complex networks.  Science, 298:824–827, 2002.
  * [210] S. Fortunato.  Community detection in graphs.  Phys. Rep., 486:75–174, 2010.
  * [211] V. Colizza, M. A. Serrano A. Flammini, and A. Vespignani.  Detecting rich-club ordering in complex networks.  Nat. Phys., 2:110–115, 2006.
  * [212] S. Kojaku and N. Masuda.  Core-periphery structure requires something else in the network.  New J. Phys., 20:043012, 2018.
  * [213] P. Expert, T. S. Evans, V. D. Blondel, and R. Lambiotte.  Uncovering space-independent communities in spatial networks.  Proc. Natl. Acad. Sci. USA, 108:7663–7668, 2011.
  * [214] T. Squartini, R. Mastrandrea, and D. Garlaschelli.  Unbiased sampling of network ensembles.  New J. Phys., 17:023052, 2015.
  * [215] T. Squartini and D. Garlaschelli.  Maximum-Entropy Networks.  Springer, Cham, Switzerland, 2017.
  * [216] E. Valdano and A. Arenas.  Exact rank reduction of network models.  Phys. Rev. X, 9:031050, 2019.
  * [217] M. E. J. Newman.  Networks.  Oxford University Press, Oxford, UK, second edition, 2018.
  * [218] N. Masuda, S. Kojaku, and Y. Sano.  Configuration model for correlation matrices preserving the node strength.  Phys. Rev. E, 98:012312, 2018.
  * [219] W. Böhm and K. Hornik.  Generating random correlation matrices by the simple rejection method: Why it does not work.  Stat. Prob. Lett., 87:27–30, 2014.
  * [220] J. Theiler, S. Eubank, A. Longtin, B. Galdrikian, and J. D. Farmer.  Testing for nonlinearity in time series: The method of surrogate data.  Physica D, 58:77–94, 1992.
  * [221] T. Schreiber and A. Schmitz.  Surrogate time series.  Physica D, 142:346–382, 2000.
  * [222] M. Shinn, A. Hu, L. Turner, S. Noble, K. H. Preller, J. L. Ji, F. Moujaes, S. Achard, D. Scheinost, R. T. Constable, J. H. Krystal, F. X. Vollenweider, D. Lee, A. Anticevic, E. T. Bullmore, and J. D. Murray.  Functional brain networks reflect spatial and temporal autocorrelation.  Nat. Neurosci., 26:867–878, 2023.
  * [223] M. Hirschberger, Y. Qi, and R. E. Steuer.  Randomly generating portfolio-selection covariance matrices with specified distributional characteristics.  Eur. J. Oper. Res., 177:1610–1625, 2007.
  * [224] A. Fornito, A. Zalesky, and M. Breakspear.  Graph analysis of the human connectome: Promise, progress, and pitfalls.  NeuroImage, 80:426–444, 2013.
  * [225] A. L. Barabási.  Network Science.  Cambridge University Press, Cambridge, UK, 2016.
  * [226] A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani.  The architecture of complex weighted networks.  Proc. Natl. Acad. Sci. USA, 101:3747–3752, 2004.
  * [227] V. M. Eguíluz, D. R. Chialvo, G. A. Cecchi, M. Baliki, and A. V. Apkarian.  Scale-free brain functional networks.  Phys. Rev. Lett., 94:018102, 2005.
  * [228] B. Zhang and S. Horvath.  A general framework for weighted gene co-expression network analysis.  Stat. Appl. Genet. Mol. Biol., 4:17, 2005.
  * [229] D. S. Bassett, E. Bullmore, B. A. Verchinski, V. S. Mattay, D. R. Weinberger, and A. Meyer-Lindenberg.  Hierarchical organization of human cortical networks in health and schizophrenia.  J. Neurosci., 28:9239–9248, 2008.
  * [230] M. A. Porter, J.-P. Onnela, and P. J. Mucha.  Communities in networks.  Notices of the AMS, 56:1082–1097, 1164–1166, 2009.
  * [231] A. Almog, M. R. Buijink, O. Roethler, S. Michel, J. H. Meijer, J. H. T. Rohling, and D. Garlaschelli.  Uncovering functional signature in neural systems via random matrix theory.  PLoS Comput. Biol., 15:e1006934, 2019.
  * [232] J. Saramäki, M. Kivelä, J.-P. Onnela, K. Kaski, and J. Kertész.  Generalizations of the clustering coefficient to weighted complex networks.  Phys. Rev. E, 75:027105, 2007.
  * [233] Y. Wang, E. Ghumare, R. Vandenberghe, and P. Dupont.  Comparison of different generalizations of clustering coefficient and local efficiency for weighted undirected graphs.  Neural Comput., 29:313–331, 2017.
  * [234] N. Masuda, M. Sakaki, T. Ezaki, and T. Watanabe.  Clustering coefficients for correlation networks.  Front. Neuroinfo., 12:7, 2018.
  * [235] phiclust: A clusterability measure for scRNA-seq data.  https://github.com/semraulab/phiclust.  Accessed: 13 November 2023.
  * [236] Mircea, M. and Hochane, M. and Fan, X. and Chuva de Sousa Lopes, S. M. and Garlaschelli, D. and Semrau, S.  Phiclust: A clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations.  https://zenodo.org/record/5785793#.Ybs5wn3MK3I.  Accessed: 13 November 2023.
  * [237] M. Rubinov and O. Sporns.  Complex network measures of brain connectivity: Uses and interpretations.  NeuroImage, 52:1059–1069, 2010.
  * [238] T. P. Peixoto.  The graph-tool python library.  http://figshare.com/articles/graph_tool/1164194.  Accessed: 14 November 2023.
  * [239] M. O. Kuismin and M. J. Sillanpää.  Estimation of covariance and precision matrix, network structure, and a view toward systems biology.  WIREs Comput. Stat., 9:e1415, 2017.
  * [240] S. Epskamp, A. O. J. Cramer, L. J. Waldorp, V. D. Schmittmann, and D. Borsboom.  qgraph: Network visualizations of relationships in psychometric data.  J. Stat. Software, 48:1–18, 2012.
  * [241] R. R. Gabriel.  Estimating a psychometric network with qgraph.  https://reisrgabriel.com/blog/2021-08-10-psych-network/.  Accessed: 24 August 2023.
  * [242] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay.  Scikit-learn: Machine learning in Python.  J. Mach. Learn. Res., 12:2825–2830, 2011.
  * [243] Scikit-learn package.  https://scikit-learn.org/stable/install.html.  Accessed: 18 May 2022.
  * [244] Mel.  Random matrix theory (rmt) filtering of financial time series for community detection.  http://www.mathworks.com/matlabcentral/fileexchange/49011.  Accessed: 13 November 2023.
  * [245] M. W. Cole, T. Yarkoni, G. Repovs, A. Anticevic, and T. S. Braver.  Global connectivity of prefrontal cortex predicts cognitive control and intelligence.  J. Neurosci., 32:8988–8999, 2012.
  * [246] E. Santarnecchi, G. Galli, N. R. Polizzotto, A. Rossi, and S. Rossi.  Efficiency of weak brain connections support general cognitive functioning.  Human Brain Mapping, 35:4566–4582, 2014.
  * [247] J.-G. Young, G. T. Cantwell, and M. E. J. Newman.  Bayesian inference of network structure from unreliable data.  J. Compl. Netw., 8:cnaa046, 2020.
  * [248] S. Boccaletti, G. Bianconi, R. Criado, C. I. del Genio, J. Gómez-Gardeñes, M. Romance, I. Sendiña Nadal, Z. Wang, and M. Zanin.  The structure and dynamics of multilayer networks.  Phys. Rep., 544:1–122, 2014.
  * [249] M. Kivelä, A. Arenas, M. Barthelemy, J. P. Gleeson, Y. Moreno, and M. A. Porter.  Multilayer networks.  J. Comp. Netw., 2:203–271, 2014.
  * [250] G. Bianconi.  Multilayer Networks.  Oxford University Press, Oxford, UK, 2018.
  * [251] M. J. Brookes, P. K. Tewarie, B. A. E. Hunt, S. E. Robson, L. E. Gascoyne, E. B. Liddle, P. F. Liddle, and P. G. Morris.  A multi-layer network approach to MEG connectivity analysis.  NeuroImage, 132:425–438, 2016.
  * [252] P. Tewarie, A. Hillebrand, B. W. van Dijk, C. J. Stam, G. C. O’Neill, P. Van Mieghem, J. M. Meier, M. W. Woolrich, P. G. Morris, and M. J. Brookes.  Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach.  NeuroImage, 142:324–336, 2016.
  * [253] J. M. Buldú and M. A. Porter.  Frequency-based brain networks: From a multiplex framework to a full multilayer description.  Netw. Neurosci., 2:418–441, 2017.
  * [254] M. De Domenico, S. Sasai, and A. Arenas.  Mapping multiplex hubs in human functional brain networks.  Front. Neurosci., 10:326, 2016.
  * [255] M. De Domenico.  Multilayer modeling and analysis of human brain networks.  GigaScience, 6:gix004, 2017.
  * [256] R. Dorantes-Gilardi, D. García-Cortés, E. Hernández-Lemus, and J. Espinal-Enríquez.  Multilayer approach reveals organizational principles disrupted in breast cancer co-expression networks.  Appl. Netw. Sci., 5:47, 2020.
  * [257] M. Russell, A. Aqil, M. Saitou, O. Gokcumen, and N. Masuda.  Gene communities in co-expression networks across different tissues.  PLoS Comput. Biol., 00:in press, 2023.
  * [258] N. Musmeci, V. Nicosia, T. Aste, T. Di Matteo, and V. Latora.  The multiplex dependency structure of financial markets.  Complexity, 2017:9586064, 2017.
  * [259] P. J. Mucha, T. Richardson, K. Macon, M. A. Porter, and J.-P. Onnela.  Community structure in time-dependent, multiscale, and multiplex networks.  Science, 328:876–878, 2010.
  * [260] D. Taylor, S. Shai, N. Stanley, and P. J. Mucha.  Enhanced detectability of community structure in multilayer networks through layer aggregation.  Phys. Rev. Lett., 116:228301, 2016.
  * [261] D. Taylor, R. S. Caceres, and P. J. Mucha.  Super-resolution community detection for layer-aggregated multilayer networks.  Phys. Rev. X, 7:031056, 2017.
  * [262] M. Venkatesh, J. Jaja, and L. Pessoa.  Comparing functional connectivity matrices: A geometry-aware approach applied to participant identification.  NeuroImage, 207:116398, 2020.
  * [263] K. You and H.-J. Park.  Re-visiting Riemannian geometry of symmetric positive definite matrices for the analysis of functional connectivity.  NeuroImage, 225:117464, 2021.
  * [264] K. Abbas, M. Liu, M. Venkatesh, E. Amico, A. D. Kaplan, M. Ventresca, L. Pessoa, J. Harezlak, and J. Goñi.  Geodesic distance on optimally regularized functional connectomes uncovers individual fingerprints.  Brain Conn., 11:333–348, 2021.
  * [265] X. Pennec, P. Fillard, and N. Ayache.  A Riemannian framework for tensor computing.  Int. J. Comput. Vis., 66:41–66, 2006.
  * [266] M. Rahim, B. Thirion, and G. Varoquaux.  Population shrinkage of covariance (PoSCE) for better individual brain functional-connectivity estimation.  Med. Image Anal., 54:138–148, 2019.
  * [267] L. Lovász.  Large Networks and Graph Limits.  American Mathematical Society, Providence, RI, 2012.
  * [268] G. Bianconi and A.-L. Barabási.  Bose-Einstein condensation in complex networks.  Phys. Rev. Lett., 86:5632–5635, 2001.
  * [269] G. Bianconi and A.-L. Barabási.  Competition and multiscaling in evolving networks.  Europhys. Lett., 54:436–442, 2001.
  * [270] K.-I. Goh, B. Kahng, and D. Kim.  Universal behavior of load distribution in scale-free networks.  Phys. Rev. Lett., 87:278701, 2001.
  * [271] G. Caldarelli, A. Capocci, P. De Los Rios, and M. A. Muñoz.  Scale-free networks from varying vertex intrinsic fitness.  Phys. Rev. Lett., 89:258702, 2002.
  * [272] M. Boguna and R. Pastor-Satorras.  Class of correlated random networks with hidden variables.  Phys. Rev. E, 68:036112, 2003.
  * [273] N. Masuda, H. Miwa, and N. Konno.  Analysis of scale-free networks based on a threshold graph with intrinsic vertex weights.  Phys. Rev. E, 70:036124, 2004.
  * [274] N. Perra, B. Gonçalves, R. Pastor-Satorras, and A. Vespignani.  Activity driven modeling of time varying networks.  Sci. Rep., 2:469, 2012.
  * [275] N. Masuda, H. Miwa, and N. Konno.  Geographical threshold graphs with small-world and scale-free properties.  Phys. Rev. E, 71:036108, 2005.
  * [276] N. Masuda and N. Konno.  VIP-club phenomenon: Emergence of elites and masterminds in social networks.  Soc. Netw., 28:297–309, 2006.
  * [277] G. T. Cantwell, Y. Liu, B. F. Maier, A. C. Schwarze, C. A. Serván, J. Snyder, and G. St-Onge.  Thresholding normally distributed data creates complex networks.  Phys. Rev. E, 101:062302, 2020.