This paper extends the possibility to examine the underlying curvature of data through the lens of topology by using the Betti curves, tools of Persistent Homology. We show that low-dimensional Betti curve approximations effectively distinguish not only Euclidean, but also spherical and hyperbolic geometric matrices, both from purely random matrices as well as among themselves. We proved this by analysing the behaviour of Betti curves for various geometric matrices - i.e distance matrices of points randomly distributed on manifolds given by the Euclidean space, the sphere, and the hyperbolic space. We further show that the standard approach to network construction gives rise to (spurious) spherical geometry, and document the role of sample size and dimension to assess real-world connectivity matrices. Finally, we observe that real-world datasets coming from neuroscience, finance and climate seem to exhibit a hyperbolic character. The potential confounding "hyperbologenic effect" of intrinsic low-rank modular structures is evaluated.

Department of Mathematics University of Bologna Bologna Italy

Institute of Computer Science of the Czech Academy of Sciences Pod Vodárenskou věží 271 2 182 07 Prague Czech Republic

National Institute of Mental Health Topolová 748 250 67 Klecany Czech Republic

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