Detecting the ultra low dimensionality of real networks

Abstract

Reducing dimension redundancy to find simplifying patterns in high-dimensional datasets and complex networks has become a major endeavor in many scientific fields. However, detecting the dimensionality of their latent space is challenging but necessary to generate efficient embeddings to be used in a multitude of downstream tasks. Here, we propose a method to infer the dimensionality of networks without the need for any a priori spatial embedding. Due to the ability of hyperbolic geometry to capture the complex connectivity of real networks, we detect ultra low dimensionality far below values reported using other approaches. We applied our method to real networks from different domains and found unexpected regularities, including: tissue-specific biomolecular networks being extremely low dimensional; brain connectomes being close to the three dimensions of their anatomical embedding; and social networks and the Internet requiring slightly higher dimensionality. Beyond paving the way towards an ultra efficient dimensional reduction, our findings help address fundamental issues that hinge on dimensionality, such as universality in critical behavior.

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Cited by in corpus (10)

• The low-rank hypothesis of complex systems
• Emergence of geometric Turing patterns in complex networks
• Random hyperbolic graphs in $d+1$ dimensions
• Fundamental interactions in self-organized critical dynamics on higher-order networks
• Exact and rapid linear clustering of networks with dynamic programming
• Scaling properties of scale-free networks in degree-thresholding renormalization flows
• Strange Attractors in Complex Networks
• Cliques in High-Dimensional Geometric Inhomogeneous Random Graphs
• Real-World Networks are Low-Dimensional: Theoretical and Practical Assessment
• Mapping bipartite networks into multidimensional hyperbolic spaces