11,661 research outputs found
Random subtrees of complete graphs
We study the asymptotic behavior of four statistics associated with subtrees
of complete graphs: the uniform probability that a random subtree is a
spanning tree of , the weighted probability (where the probability a
subtree is chosen is proportional to the number of edges in the subtree) that a
random subtree spans and the two expectations associated with these two
probabilities. We find and both approach ,
while both expectations approach the size of a spanning tree, i.e., a random
subtree of has approximately edges
The looping rate and sandpile density of planar graphs
We give a simple formula for the looping rate of loop-erased random walk on a
finite planar graph. The looping rate is closely related to the expected amount
of sand in a recurrent sandpile on the graph. The looping rate formula is
well-suited to taking limits where the graph tends to an infinite lattice, and
we use it to give an elementary derivation of the (previously computed) looping
rate and sandpile densities of the square, triangular, and honeycomb lattices,
and compute (for the first time) the looping rate and sandpile densities of
many other lattices, such as the kagome lattice, the dice lattice, and the
truncated hexagonal lattice (for which the values are all rational), and the
square-octagon lattice (for which it is transcendental)
Sampling Random Spanning Trees Faster than Matrix Multiplication
We present an algorithm that, with high probability, generates a random
spanning tree from an edge-weighted undirected graph in
time (The notation hides
factors). The tree is sampled from a distribution
where the probability of each tree is proportional to the product of its edge
weights. This improves upon the previous best algorithm due to Colbourn et al.
that runs in matrix multiplication time, . For the special case of
unweighted graphs, this improves upon the best previously known running time of
for (Colbourn
et al. '96, Kelner-Madry '09, Madry et al. '15).
The effective resistance metric is essential to our algorithm, as in the work
of Madry et al., but we eschew determinant-based and random walk-based
techniques used by previous algorithms. Instead, our algorithm is based on
Gaussian elimination, and the fact that effective resistance is preserved in
the graph resulting from eliminating a subset of vertices (called a Schur
complement). As part of our algorithm, we show how to compute
-approximate effective resistances for a set of vertex pairs via
approximate Schur complements in time,
without using the Johnson-Lindenstrauss lemma which requires time. We
combine this approximation procedure with an error correction procedure for
handing edges where our estimate isn't sufficiently accurate
Spanning trees of graphs on surfaces and the intensity of loop-erased random walk on planar graphs
We show how to compute the probabilities of various connection topologies for
uniformly random spanning trees on graphs embedded in surfaces. As an
application, we show how to compute the "intensity" of the loop-erased random
walk in , that is, the probability that the walk from (0,0) to
infinity passes through a given vertex or edge. For example, the probability
that it passes through (1,0) is 5/16; this confirms a conjecture from 1994
about the stationary sandpile density on . We do the analogous
computation for the triangular lattice, honeycomb lattice and , for which the probabilities are 5/18, 13/36, and
respectively.Comment: 45 pages, many figures. v2 has an expanded introduction, a revised
section on the LERW intensity, and an expanded appendix on the annular matri
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