Derandomizing Directed Random Walks in Almost-Linear Time

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In this article, we present the first deterministic directed Laplacian L systems solver that runs in time almost-linear in the number of non-zero entries of L. Previous reductions imply the first deterministic almost-linear time algorithms for computing various fundamental quantities on directed graphs including stationary distributions, personalized PageRank, hitting times and escape probabilities.
We obtain these results by introducing partial symmetrization, a new technique that makes the Laplacian of an Eulerian directed graph ``less directed'' in a useful sense, which may be of independent interest. The usefulness of this technique comes from two key observations: Firstly, the partially symmetrized Laplacian preconditions the original Eulerian Laplacian well in Richardson iteration, enabling us to construct a solver for the original matrix from a solver for the partially symmetrized one. Secondly, the undirected structure in the partially symmetrized Laplacian makes it possible to sparsify the matrix very crudely, i.e. with large spectral error, and still show that Richardson iterations convergence when using the sparsified matrix as a preconditioner. This allows us to develop deterministic sparsification tools for the partially symmetrized Laplacian.
Together with previous reductions from directed Laplacians to Eulerian Laplacians, our technique results in the first deterministic almost-linear time algorithm for solving linear equations in directed Laplacians. To emphasize the generality of our new technique, we show that two prominent existing (randomized) frameworks for solving linear equations in Eulerian Laplacians can be derandomized in this way: the squaring-based framework of Cohen, Kelner, Peebles, Peng, Rao, Sidford and Vladu (STOC 2017) and the sparsified Cholesky-based framework of Peng and Song (STOC 2022).