Efficiently Generating Geometric Inhomogeneous and Hyperbolic Random Graphs

TL;DR

This paper introduces the first efficient, linear-time generator for geometric inhomogeneous random graphs (GIRGs), capable of producing large-scale graphs with controllable properties, and adapts it for hyperbolic random graphs (HRGs).

Contribution

It presents the fastest sequential GIRG generator supporting non-zero temperatures and higher dimensions, with an efficient method to set expected average degree, and compares GIRGs and HRGs.

Findings

Generator can produce 10 million edges in under a second.

Supports non-zero temperatures and higher-dimensional geometries.

GIRGs and HRGs are closely related but not equivalent in practice.

Abstract

Hyperbolic random graphs (HRG) and geometric inhomogeneous random graphs (GIRG) are two similar generative network models that were designed to resemble complex real world networks. In particular, they have a power-law degree distribution with controllable exponent $\beta$, and high clustering that can be controlled via the temperature $T$. We present the first implementation of an efficient GIRG generator running in expected linear time. Besides varying temperatures, it also supports underlying geometries of higher dimensions. It is capable of generating graphs with ten million edges in under a second on commodity hardware. The algorithm can be adapted to HRGs. Our resulting implementation is the fastest sequential HRG generator, despite the fact that we support non-zero temperatures. Though non-zero temperatures are crucial for many applications, most existing generators are restricted to $T = 0$. Our generators support parallelization, although this is not the focus of this paper. We note that our generators draw from the correct probability distribution, i.e., they involve no approximation. Besides the generators themselves, we also provide an efficient algorithm to determine the non-trivial dependency between the average degree of the resulting graph and the input parameters of the GIRG model. This makes it possible to specify the expected average degree as input. Moreover, we investigate the differences between HRGs and GIRGs, shedding new light on the nature of the relation between the two models. Although HRGs represent, in a certain sense, a special case of the GIRG model, we find that a straight-forward inclusion does not hold in practice. However, the difference is negligible for most use cases.