Random Graphons and a Weak Positivstellensatz for Graphs

TL;DR

This paper explores various equivalent structures describing graph limits, introduces relaxed versions, and proves an analogue of the Positivstellensatz for graphs, linking inequalities in subgraph densities to sum-of-squares proofs.

Contribution

It establishes the equivalence of relaxed graph limit structures and proves a Positivstellensatz analogue, connecting subgraph density inequalities to sum-of-squares representations.

Findings

Equivalent relaxed structures for graph limits are identified.

A probability measure-based characterization of graph limits is provided.

An analogue of the Positivstellensatz for graphs is proven.

Abstract

In an earlier paper the authors proved that limits of convergent graph sequences can be described by various structures, including certain 2-variable real functions called graphons, random graph models satisfying certain consistency conditions, and normalized, multiplicative and reflection positive graph parameters. In this paper we show that each of these structures has a related, relaxed version, which are also equivalent. Using this, we describe a further structure equivalent to graph limits, namely probability measures on countable graphs that are ergodic with respect to the group of permutations of the nodes. As an application, we prove an analogue of the Positivstellensatz for graphs: We show that every linear inequality between subgraph densities that holds asymptotically for all graphs has a formal proof in the following sense: it can be approximated arbitrarily well by another valid inequality that is a "sum of squares" in the algebra of partially labeled graphs.