Right-convergence of sparse random graphs
David Gamarnik

TL;DR
This paper proves the right-convergence of sparse Erdős-Rényi graphs for certain target graphs W, addressing fundamental difficulties in sparse graph limits and confirming a special case of Talagrand's conjecture using interpolation techniques.
Contribution
It establishes the right-convergence of sparse random graphs for target graphs W with a convexity property, extending previous results and confirming a case of Talagrand's conjecture.
Findings
Proves right-convergence for sparse Erdős-Rényi graphs with convexity conditions.
Addresses the existence of free energy limits in sparse graph models.
Confirms a special case of Talagrand's conjecture regarding measure limits.
Abstract
The paper is devoted to the problem of establishing right-convergence of sparse random graphs. This concerns the convergence of the logarithm of number of homomorphisms from graphs or hyper-graphs to some target graph . The theory of dense graph convergence, including random dense graphs, is now well understood, but its counterpart for sparse random graphs presents some fundamental difficulties. Phrased in the statistical physics terminology, the issue is the existence of the log-partition function limits, also known as free energy limits, appropriately normalized for the Gibbs distribution associated with . In this paper we prove that the sequence of sparse \ER graphs is right-converging when the tensor product associated with the target graph satisfies certain convexity property. We treat the case of discrete and continuous target graphs . The latter case…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsLimits and Structures in Graph Theory · Graph theory and applications · Stochastic processes and statistical mechanics
