Sampling Geometric Inhomogeneous Random Graphs in Linear Time

TL;DR

This paper introduces a new geometric inhomogeneous random graph model, GIRGs, which simplifies hyperbolic random graphs and provides efficient linear-time sampling, with proven structural properties like high clustering and small separators.

Contribution

The paper presents a linear-time sampling algorithm for GIRGs, a simplified model of hyperbolic random graphs, and establishes key structural properties.

Findings

Sampling in expected linear time, improving previous methods

GIRGs have clustering coefficients in Omega(1)

GIRGs possess small separators and can be efficiently compressed

Abstract

Real-world networks, like social networks or the internet infrastructure, have structural properties such as large clustering coefficients that can best be described in terms of an underlying geometry. This is why the focus of the literature on theoretical models for real-world networks shifted from classic models without geometry, such as Chung-Lu random graphs, to modern geometry-based models, such as hyperbolic random graphs. With this paper we contribute to the theoretical analysis of these modern, more realistic random graph models. Instead of studying directly hyperbolic random graphs, we use a generalization that we call geometric inhomogeneous random graphs (GIRGs). Since we ignore constant factors in the edge probabilities, GIRGs are technically simpler (specifically, we avoid hyperbolic cosines), while preserving the qualitative behaviour of hyperbolic random graphs, and we suggest to replace hyperbolic random graphs by this new model in future theoretical studies. We prove the following fundamental structural and algorithmic results on GIRGs. (1) As our main contribution we provide a sampling algorithm that generates a random graph from our model in expected linear time, improving the best-known sampling algorithm for hyperbolic random graphs by a substantial factor O(n^0.5). (2) We establish that GIRGs have clustering coefficients in {\Omega}(1), (3) we prove that GIRGs have small separators, i.e., it suffices to delete a sublinear number of edges to break the giant component into two large pieces, and (4) we show how to compress GIRGs using an expected linear number of bits.