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Successive minimum spanning trees

Authors: Svante Janson; Gregory B. Sorkin;

Successive minimum spanning trees

Abstract

AbstractIn a complete graphwith independent uniform(or exponential) edge weights, letbe the minimum‐weight spanning tree (MST), andthe MST after deleting the edges of all previous trees. We show that each tree's weightconverges in probability to a constant, with, and we conjecture that. The problem is distinct from Frieze and Johansson's minimum combined weightofkedge‐disjoint spanning trees; indeed,. With an edge of weightw“arriving” at time, Kruskal's algorithm defines forests, initially empty and eventually equal to, each edge added to the first possible. Using tools of inhomogeneous random graphs we obtain structural results including that the fraction of vertices in the largest component ofconverges to some. We conjecture that the functionstend to time translations of a single function.

Countries
Germany, United Kingdom
Keywords

inhomogeneous random graph, miminum spanning tree, functional fixed point, robust optimization, Probabilistic methods in extremal combinatorics, including polynomial methods (combinatorial Nullstellensatz, etc.), second-cheapest structure, minimum spanning tree, 510, Trees, Signed and weighted graphs, FOS: Mathematics, Mathematics - Combinatorics, 05C80, 60C05, 05C22, 68W40, Kruskal’s algorithm, Combinatorial probability, Probability (math.PR), 004, multi-type branching process, Kruskal's algorithm, discrete probability, Combinatorics (math.CO), optimization in random structures, Mathematics - Probability, ddc: ddc:004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
hybrid