Star observations in bounded-degree graphs

TL;DR

This paper demonstrates that local-global convergence in bounded-degree graphs and graphings can be fully characterized by small, simple observations like colored star and cherry statistics, simplifying the analysis of large networks.

Contribution

It introduces the colored cherry metric and proves its equivalence to existing metrics, enabling reconstruction of the local-global structure from minimal observations.

Findings

Colored degree distributions determine local-global convergence.

Colored cherry metric induces the same topology as more complex metrics.

Small observations suffice to capture the full local-global structure.

Abstract

Similarity metrics are central in the theory of large networks and graph limits. For bounded-degree graphs, the Benjamini--Schramm metric records the distribution of rooted neighbourhoods, while the stronger colored-neighbourhood metric gives rise to local-global convergence. In this paper we show that this intricate topology is already determined by much smaller observations. For technical convenience and greater generality, we work with graphings, which are measurable generalizations of finite graphs and include all finite graphs as special cases. We prove that, for graphings of uniformly bounded degree, convergence of all colored degree distributions, or equivalently of all colored star statistics, is equivalent to local-global convergence. We also introduce an even more economical sampling procedure, the colored cherry metric, in which one observes only the root and two randomly chosen neighbours, and prove that it induces the same topology. Thus the full local-global structure can be reconstructed, at the level of topology, from families of very small colored observations. Our star-observation theorem was previously announced in the work of Backhausz and the author as an important ingredient in the proof that the so-called action convergence unifies dense graph limit theory with local-global convergence, thereby providing a general graph limit theory for sparse, dense, and intermediate-density graphs.