Real networks are geometrically broken. The hubs and heavy tails that Barabási discovered are not accidents of network growth. They are symptoms of a deeper problem: networks live in a curved, hyperbolic space, but we have been measuring them with tools built for flat space. The result is systematic distortion in almost every classical network measurement ever made. This work builds a three-part remedy. First, we construct an isometric canvas by ironing out the crumples of network space using Ricci flow, and introduce the Pythagorean tensor as a simple curvature ruler that works at any scale. Second, we bring four diagnostic instruments to this canvas that reveal hidden structure without making any assumptions about what the network contains. Third, we derive the algebraic laws that govern stable network configurations and show that several phenomena currently treated as empirical surprises are in fact geometric theorems. The heavy tail is not a mystery. It is a consequence of the shape of the space.
Avishai Roif (Thu,) studied this question.