The geometric nature of weights in real complex networks

TLDR

Many real complex networks are conjectured to be embedded in hidden metric spaces, where node distances encode connection likelihood, providing a geometrical interpretation that informs Internet routing, biochemical pathway hierarchy, and international trade evolution. The study provides empirical evidence that this geometric interpretation extends to the weighted organization of real complex networks. A general, versatile model is introduced to quantify the coupling between topology, weights, and an underlying metric space. The model accurately reproduces both topology and weights, indicating that connection formation and weight assignment are governed by distinct processes.

Abstract

Abstract The topology of many real complex networks has been conjectured to be embedded in hidden metric spaces, where distances between nodes encode their likelihood of being connected. Besides of providing a natural geometrical interpretation of their complex topologies, this hypothesis yields the recipe for sustainable Internet’s routing protocols, sheds light on the hierarchical organization of biochemical pathways in cells, and allows for a rich characterization of the evolution of international trade. Here we present empirical evidence that this geometric interpretation also applies to the weighted organization of real complex networks. We introduce a very general and versatile model and use it to quantify the level of coupling between their topology, their weights and an underlying metric space. Our model accurately reproduces both their topology and their weights, and our results suggest that the formation of connections and the assignment of their magnitude are ruled by different processes.