Convex minorant trees associated with Brownian paths and the continuum limit of the minimum spanning tree
Nicolas Broutin, Jean-Fran\c{c}ois Marckert

TL;DR
This paper constructs the scaling limit of the minimum spanning tree of a complete graph using convex minorants of Brownian motion, revealing new structural insights and connections to continuum random trees.
Contribution
It introduces the Brownian parabolic tree as a new representation of the MST limit and develops the theory of convex minorant trees associated with Brownian-like paths.
Findings
Hausdorff dimension of the Brownian parabolic tree is almost surely 3
Constructs a metric multiplicative coalescent linking different graph scales
Shows convex minorant trees include Brownian continuum random trees
Abstract
We give an explicit construction of the scaling limit of the minimum spanning tree of the complete graph. The limit object is described using a recursive construction involving the convex minorants of a Brownian motion with parabolic drift (and countably many i.i.d. uniform random variables); we call it the Brownian parabolic tree. Aside from the new representation, this point of view has multiple consequences. For instance, it permits us to prove that its Hausdorff dimension is almost surely 3. It also intrinsically contains information related to some underlying dynamics: one notable by-product is the construction of a standard metric multiplicative coalescent which couples the scaling limits of random graphs at different points of the critical window in terms of the same simple building blocks. The above results actually fit in a more general framework. They result from the…
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
TopicsStochastic processes and statistical mechanics · Markov Chains and Monte Carlo Methods · Geometry and complex manifolds
