Geometric Routing in Geometric Inhomogeneous Random Graphs

2026-06-01

AI Summary

This study investigates whether efficient decentralized routing is achievable in geometric inhomogeneous random graphs (GIRGs) using only geometric information, without access to node weights. Employing probabilistic graph theory and a two-phase trajectory modeling approach, the authors rigorously establish—for the first time—that when the power-law exponent τ lies in (2,3) and the geometric decay parameter α exceeds τ−1, purely geometric routing succeeds with constant probability in finding paths of length Θ(log log n). This result demonstrates that geometric information alone is sufficient to guide messages efficiently toward the high-weight core, thereby matching the asymptotically optimal performance of greedy routing without requiring explicit knowledge of node weights.

Abstract

We present the first rigorous analysis of decentralized geometric routing in Geometric Inhomogeneous Random Graphs (GIRGs), a weight-agnostic variant of the greedy routing protocol. While greedy routing in GIRGs is known to explain the algorithmic small-world phenomenon by finding ultra-short paths of length $Θ(\log \log n)$, it assumes additional knowledge of vertex weights beyond geometry, an assumption that is often restrictive or unavailable. We investigate whether the underlying geometry alone is sufficient for efficient navigation. We prove that for power-law weight exponent $τ\in (2,3)$ and geometric decay parameter $α> τ- 1$, geometric routing succeeds with constant probability and finds ultra-short paths of length $Θ(\log \log n)$, matching the optimal asymptotic guarantees for greedy routing. Our analysis further reveals that, upon success, both protocols follow a similar two-phase trajectory, consisting of a rapid ascent to the heavy vertices, followed by efficient navigation to the target. These results demonstrate that, in the appropriate regime, the network's geometry alone implicitly guides the path to the target through its high-weight core.

Problem

• Geometric Routing
• Geometric Inhomogeneous Random Graphs
• Greedy Routing
• Algorithmic Small-World
• Network Geometry

Innovation

• Geometric Routing
• Geometric Inhomogeneous Random Graphs
• Greedy Routing
• Small-World Phenomenon
• Power-Law Networks