Walking randomly, massively, and efficiently
Jakub Lacki, Slobodan Mitrovic, Krzysztof Onak, Piotr Sankowski
Abstract
We introduce a set of techniques that allow for efficiently generating many independent random walks in the Massive Parallel Computation (MPC) model with space per machine strongly sublinear in the number of vertices. In this space-per-machine regime, many natural approaches to graph problems struggle to overcome the Θ(log n) MPC round complexity barrier. Our techniques enable breaking this barrier for PageRank-one of the most important applications of random walks-even in more challenging directed graphs, and for approximate bipartiteness and expansion testing.
In the undirected case, we start our random walks from the stationary distribution, which implies that we approximately know the empirical distribution of their next steps. This allows for preparing continuations of random walks in advance and applying a doubling approach. As a result we can generate multiple random walks of length l in Θ(log l) rounds on MPC. Moreover, we show that under the popular 1-vs.-2-Cycles conjecture, this round complexity is asymptotically tight.
For directed graphs, our approach stems from our treatment of the PageRank Markov chain. We first compute the PageRank for the undirected version of the input graph and then slowly transition towards the directed case, considering convex combinations of the transition matrices in the process.
For PageRank, we achieve the following round complexities for damping factor equal to 1ǫ:
• in O(log log n + log 1/ǫ) rounds for undirected graphs (with Õ(m/ǫ 2 ) total space),
• in Õ(log 2 log n + log 2 1/ǫ) rounds for directed graphs (with Õ((m + n 1+o(1) )/poly ǫ) total space).
The round complexity of our result for computing PageRank has only logarithmic dependence on 1/ǫ. We use this to show that our PageRank algorithm can be used to construct directed length-l random walks in O(log 2 log n + log 2 l) rounds (with Õ((m + n 1+o(1) )poly l) total space). Namely, by letting ǫ = Θ(1/l), a length-l PageRank walk with constant probability contains no random jump, and hence is a directed random walk.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers6
- Deterministic massively parallel connectivitySam Coy, Artur CzumajSTOC 2022 · 10 citations
- Flock: A Knowledge Graph Foundation Model via Learning on Random WalksJinwoo Kim, Xingyue Huang, Krzysztof Olejniczak, Kyungbin Min et al.ICLR 2026 · 8 citations
- Iterative Methods via Locally Evolving Set ProcessBaojian Zhou, Yifan Sun, Reza Babanezhad Harikandeh, Xingzhi Guo et al.NeurIPS 2024 · 4 citations
- Efficient and Local Parallel Random WalksMichael Kapralov, Silvio Lattanzi, Navid Nouri, Jakab TardosNeurIPS 2021 · 3 citations
- PageRank Centrality in Directed Graphs with Bounded In-DegreeMikkel Thorup, Hanzhi Wang, Zhewei Wei, Mingji YangSODA 2026
Related papers
- Simulating Random Walks in Random StreamsJohn Kallaugher, Michael Kapralov, Eric PriceSODA 2022 · 3 citations
- Massively Parallel Algorithms for Personalized PageRankGuanhao Hou, Xingguang Chen, Sibo Wang, Zhewei WeiVLDB 2021 · 46 citations
- When Connectivity Is Hard, Random Walks Are Easy with Non-determinismDean Doron, Edward Pyne, Roei Tell, R. Ryan WilliamsSTOC 2025 · 4 citations
- Massively Parallel Minimum Spanning Tree in General Metric SpacesAmir Azarmehr, Soheil Behnezhad, Rajesh Jayaram, Jakub Lacki et al.SODA 2025 · 4 citations
- Lower Bounds for Non-adaptive Local Computation AlgorithmsAmir Azarmehr, Soheil Behnezhad, Alma Ghafari, Madhu SudanFOCS 2025 · 2 citations
