# Constructing networks by filtering correlation matrices: a null model approach

- Published: 13 Nov 2019 Journal: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, volume 475, page 20,190,578 (issn: 1364-5021, eissn: 1471-2946, Copyright policy)
- Publisher: The Royal Society

[1] J.-P. Onnela, K. Kaski, and J. Kertész. Clustering and information in correlation based financial networks. Eur. Phys. J. B, 38:353-362, 2004. [OpenAIRE]

[2] C. K. Tse, J. Liu, and F. C. M. Lau. A network perspective of the stock market. J. Emp. Fin., 17:659-667, 2010.

[3] M. E. J. Newman. Networks, 2nd edition. Oxford University Press, Oxford, 2018.

[4] M. Rubinov and O. Sporns. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52:1059-1069, 2010.

[5] 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.

[6] M. Zanin, P. Sousa, D. Papo, R. Bajo, J. García-Prieto, F. del Pozo, E. Menasalvas, and S. Boccaletti. Optimizing functional network representation of multivariate time series. Sci. Rep., 2:630, 2012.

[7] F. De Vico Fallani, J. Richiardi, M. Chavez, and S. Achard. Graph analysis of functional brain networks: practical issues in translational neuroscience. Phil. Trans. Royal Soc. B: Biological Sciences, 369:20130521, 2014. [OpenAIRE]

[8] F. Kose, W. Weckwerth, T. Linke, and O. Fiehn. Visualizing plant metabolomic correlation networks using clique-metabolite matrices. Bioinformatics, 17:1198-1208, 2001. [OpenAIRE]

[9] R. N. Mantegna. Hierarchical structure in financial markets. Eur. Phys. J. B, 11:193-197, 1999.

[10] 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. [OpenAIRE]

[11] V. De Vico Fallani, F.and Latora and M. Chavez. A topological criterion for filtering information in complex brain networks. PLOS Comput. Bio., 13:e1005305, 2017. [OpenAIRE]

[12] D. A. Jackson and K. M. Somers. The spectre of 'spurious' correlations. Oecologia, 86:147-151, 1991. [OpenAIRE]

[13] T. Vigen. Spurious correlations. Hachette books, 2015.

[14] J. P. Bouchaud and M. Potters. Theo. Fin. Risk Derivative Pricing, 2nd edition. Cambridge University Press, Weinheim, 2003.

[15] M. MacMahon and D. Garlaschelli. Community detection for correlation matrices. Phys. Rev. X, 5:021006, 2015.

[1] J.-P. Onnela, K. Kaski, and J. Kertész. Clustering and information in correlation based financial networks. Eur. Phys. J. B, 38:353-362, 2004. [OpenAIRE]

[2] C. K. Tse, J. Liu, and F. C. M. Lau. A network perspective of the stock market. J. Emp. Fin., 17:659-667, 2010.

[3] M. E. J. Newman. Networks, 2nd edition. Oxford University Press, Oxford, 2018.

[4] M. Rubinov and O. Sporns. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52:1059-1069, 2010.

[5] 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.

[6] M. Zanin, P. Sousa, D. Papo, R. Bajo, J. García-Prieto, F. del Pozo, E. Menasalvas, and S. Boccaletti. Optimizing functional network representation of multivariate time series. Sci. Rep., 2:630, 2012.

[7] F. De Vico Fallani, J. Richiardi, M. Chavez, and S. Achard. Graph analysis of functional brain networks: practical issues in translational neuroscience. Phil. Trans. Royal Soc. B: Biological Sciences, 369:20130521, 2014. [OpenAIRE]

[8] F. Kose, W. Weckwerth, T. Linke, and O. Fiehn. Visualizing plant metabolomic correlation networks using clique-metabolite matrices. Bioinformatics, 17:1198-1208, 2001. [OpenAIRE]

[9] R. N. Mantegna. Hierarchical structure in financial markets. Eur. Phys. J. B, 11:193-197, 1999.

[10] 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. [OpenAIRE]

[11] V. De Vico Fallani, F.and Latora and M. Chavez. A topological criterion for filtering information in complex brain networks. PLOS Comput. Bio., 13:e1005305, 2017. [OpenAIRE]

[12] D. A. Jackson and K. M. Somers. The spectre of 'spurious' correlations. Oecologia, 86:147-151, 1991. [OpenAIRE]

[13] T. Vigen. Spurious correlations. Hachette books, 2015.

[14] J. P. Bouchaud and M. Potters. Theo. Fin. Risk Derivative Pricing, 2nd edition. Cambridge University Press, Weinheim, 2003.

[15] M. MacMahon and D. Garlaschelli. Community detection for correlation matrices. Phys. Rev. X, 5:021006, 2015.