Directed Random Geometric Graphs

TL;DR

The paper introduces the Directed Random Geometric Graph (DRGG) model, capturing key properties of real-world directed networks like scale-free degree distributions, high clustering, and small-world characteristics.

Contribution

It presents a novel directed geometric graph model with proven properties matching real-world network features, supported by empirical observations.

Findings

DRGG is scale-free in indegree distribution

DRGG exhibits high clustering coefficient

Empirical word association networks resemble DRGG properties

Abstract

Many real-world networks are intrinsically directed. Such networks include activation of genes, hyperlinks on the internet, and the network of followers on Twitter among many others. The challenge, however, is to create a network model that has many of the properties of real-world networks such as powerlaw degree distributions and the small-world property. To meet these challenges, we introduce the \textit{Directed} Random Geometric Graph (DRGG) model, which is an extension of the random geometric graph model. We prove that it is scale-free with respect to the indegree distribution, has binomial outdegree distribution, has a high clustering coefficient, has few edges and is likely small-world. These are some of the main features of aforementioned real world networks. We empirically observe that word association networks have many of the theoretical properties of the DRGG model.