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Linking and Cutting Spanning Trees

Linking and cutting spanning trees
Authors: Luís M. S. Russo; Andreia Sofia Teixeira; Alexandre P. Francisco;

Linking and Cutting Spanning Trees

Abstract

We consider the problem of uniformly generating a spanning tree for an undirected connected graph. This process is useful for computing statistics, namely for phylogenetic trees. We describe a Markov chain for producing these trees. For cycle graphs, we prove that this approach significantly outperforms existing algorithms. For general graphs, experimental results show that the chain converges quickly. This yields an efficient algorithm due to the use of proper fast data structures. To obtain the mixing time of the chain we describe a coupling, which we analyze for cycle graphs and simulate for other graphs.

Keywords

FOS: Computer and information sciences, spanning tree, Discrete Mathematics (cs.DM), Industrial engineering. Management engineering, Markov chain, QA75.5-76.95, T55.4-60.8, Markov chains (discrete-time Markov processes on discrete state spaces), mixing time, Random walks on graphs, Electronic computers. Computer science, Graph algorithms (graph-theoretic aspects), Computational methods in Markov chains, Computer Science - Data Structures and Algorithms, uniform generation, Numerical analysis or methods applied to Markov chains, Data Structures and Algorithms (cs.DS), link-cut tree, Computer Science - Discrete Mathematics

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selected citations
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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!
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