Meeting times on graphs in near-cubic time
TLDR
A new algorithm drastically speeds up calculating meeting times for random walkers on graphs, reducing complexity from O(N^6) to near O(N^3).
Key contributions
- Develops an algorithm for meeting times of random walkers on graphs, reducing O(N^6) to O(N^4).
- Achieves a theoretical speedup to O(N^3 log^2 N) by leveraging Cauchy matrix structure.
- Generalizes the method to solve the Poisson equation for absorbing "lazy" pair walks.
- Provides improved algorithms for fixation probabilities and mean trait frequencies in evolutionary dynamics.
Why it matters
This paper offers a substantial breakthrough in graph theory by dramatically improving the efficiency of calculating meeting times. Its novel algorithms reduce a long-standing O(N^6) problem to near-cubic time, enabling faster analysis in fields like evolutionary dynamics and network science.
Original Abstract
The expected meeting time of two random walkers on an undirected graph of size $N$, where at each time step one walker moves and the process stops when they collide, satisfies a system of $\binom{N}{2}$ linear equations. Naïvely, solving this system takes $O\left(N^{6}\right)$ operations. However, this system of linear equations has nice structure in that it is almost a Sylvester equation, with the obstruction being a diagonal absorption constraint. We give a simple algorithm for solving this system that exploits this structure, leading to $O\left(N^{4}\right)$ operations and $Θ\left(N^{2}\right)$ space for exact computation of all $\binom{N}{2}$ meeting times. While this practical method uses only standard dense linear algebra, it can be improved (in theory) to $O\left(N^{3}\log^{2}N\right)$ operations by exploiting the Cauchy structure of the diagonal correction. We generalize this result slightly to cover the Poisson equation for the absorbing "lazy" pair walk with an arbitrary source, which can be solved at the same cost, with $O\left(N^{3}\right)$ per additional source on the same graph. We conclude with applications to evolutionary dynamics, giving improved algorithms for calculating fixation probabilities and mean trait frequencies.
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