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Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More

Michael B. Cohen,Jonathan A. Kelner,John Peebles,Richard Peng,Aaron Sidford,Adrian Vladu

Published 2016 in IEEE Annual Symposium on Foundations of Computer Science

ABSTRACT

In this paper, we provide faster algorithms for computing various fundamental quantities associated with random walks on a directed graph, including the stationary distribution, personalized PageRank vectors, hitting times, and escape probabilities. In particular, on a directed graph with n vertices and m edges, we show how to compute each quantity in time Õ(m3/4n + mn2/3), where the Õ notation suppresses polylog factors in n, the desired accuracy, and the appropriate condition number (i.e. the mixing time or restart probability). Our result improves upon the previous fastest running times for these problems; previous results either invoke a general purpose linear system solver on a n × n matrix with m nonzero entries, or depend polynomially on the desired error or natural condition number associated with the problem (i.e. the mixing time or restart probability). For sparse graphs, we obtain a running time of Õ(n7/4), breaking the O(n2) barrier of the best running time one could hope to achieve using fast matrix multiplication. We achieve our result by providing a similar running time improvement for solving directed Laplacian systems, a natural directed or asymmetric analog of the well studied symmetric or undirected Laplacian systems. We show how to solve such systems in time Õ(m3/4n + mn2/3), and efficiently reduce a broad range of problems to solving Õ(1) directed Laplacian systems on Eulerian graphs. We hope these results and our analysis open the door for further study into directed spectral graph theory.

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