Fast Distributed PageRank Computation

  title={Fast Distributed PageRank Computation},
  author={Atish Das Sarma and Anisur Rahaman Molla and Gopal Pandurangan and Eli Upfal},

Distributed Randomized Algorithms for PageRank Based on a Novel Interpretation

Gossip-type randomization is employed in the update schemes, and it is shown that the page selection need not be limited to the uniform distribution.

PageRank Computation via Web Aggregation in Distributed Randomized Algorithms

  • Atsushi SuzukiH. Ishii
  • Computer Science, Mathematics
    2019 IEEE 58th Conference on Decision and Control (CDC)
  • 2019
This paper presents extensions of the distributed algorithms which were recently proposed for the computation of PageRank that are modified for aggregation-based computation by grouping pages in the same domain.

Deterministic Coresets for Stochastic Matrices with Applications to Scalable Sparse PageRank

The PageRank algorithm is used by search engines to rank websites in their search results according to a stable state and not according to the previous local measurement of inner/outer edges from a node that may be manipulated more easily than the corresponding entry in the stable state.

Distributed PageRank Computation: An Improved Theoretical Study

Improved distributed algorithms for computing PageRank are presented and it is shown that the algorithm can be adapted to efficiently compute another variant of PageRank, i.e., the batch one-hop Personalized PageRanks, in O(log logn) communication rounds.

Stochastic PageRank maintenance over shared-nothing architectures

This work bridges the gap by proposing the first known efficient stochastic algorithm for PageRank maintenance over distributed shared-nothing architectures, and shows the efficiency and accuracy of the proposed approach, and its superiority compared to the state-of-the-art competitors.

Distributed Randomized Algorithms for PageRank Computation: Recent Advances

This chapter introduces a new class of distributed algorithms for PageRank based on a simple but novel interpretation and demonstrates that these algorithms have significant advantages in their convergence performances in comparison with other existing techniques.

Efficient PageRank Computation via Distributed Algorithms with Web Clustering

This paper proposes a clustering-based scheme, in which groups of pages make updates by locally interacting among themselves many times to expedite the convergence of PageRank, which has significant advantages in their convergence performance.

Massively Parallel Algorithms for Personalized PageRank

Delta-Push is an efficient framework for single-source and top-k PPR queries in distributed settings that reduces the number of rounds while guaranteeing that the load, i.e., the maximum number of messages an executor sends or receives in a round, can be bounded by the capacity of each executor.

Improved Communication Cost in Distributed PageRank Computation - A Theoretical Study

A new algorithm is provided that uses asymptotically the same communication round complexity while using only O(d log n) bits of bandwidth.

Edge-based Local Push for Personalized PageRank

The proposed EdgePush algorithm is a novel method for computing SSPPR queries on weighted graphs that decomposes the aforementioned push operations in edge-based push, allowing the algorithm to operate at the edge level granularity, and flexibly distribute the probabilities according to edge weights.



Distributed page ranking in structured P2P networks

Open system PageRank is presented based on the traditional PageRank used by Google, and indirect transmission is introduced to reduce communication overhead between page rankers and to achieve scalable communication.

Fast personalized PageRank on MapReduce

It is shown that the number of MapReduce iterations used by the algorithm is optimal among a broad family of algorithms for the problem, and its I/O efficiency is much better than the existing candidates.

Distributed pagerank for P2P systems

This paper defines and describes a fully distributed implementation of Google's highly effective pagerank algorithm, for "peer to peer" (P2P) systems, based on chaotic (asynchronous) iterative solution of linear systems, which provided approximately a ten-fold reduction in network traffic for two-word and three-word querying.

Fast Incremental and Personalized PageRank

The overall result is that this algorithm is fast enough for real-time queries over a dynamic social network.

Deeper Inside PageRank

A comprehensive survey of all issues associated with PageRank, covering the basic PageRank model, available and recommended solution methods, storage issues, existence, uniqueness, and convergence properties, possible alterations to the basic model, and suggested alternatives to the traditional solution methods.

Monte Carlo Methods in PageRank Computation: When One Iteration is Sufficient

This work proposes and analyzes Monte Carlo-type methods for the PageRank computation and suggests several advantages of the probabilistic Monte Carlo methods over the deterministic power iteration method.

Local Graph Partitioning using PageRank Vectors

An improved algorithm for computing approximate PageRank vectors, which allows us to find a cut with conductance at most oslash and approximately optimal balance in time O(m log4 m/oslash) in time proportional to its size.

A Survey on PageRank Computing

  • P. Berkhin
  • Computer Science, Mathematics
    Internet Math.
  • 2005
The theoretical foundations of the PageRank formulation are examined, the acceleration of PageRank computing, in the effects of particular aspects of web graph structure on the optimal organization of computations, and in PageRank stability.

Inside PageRank

A circuit analysis is introduced that allows to understand the distribution of the page score, the way different Web communities interact each other, the role of dangling pages (pages with no outlinks), and the secrets for promotion of Web pages.

Estimating PageRank on graph streams

In the streaming model, this article shows how to perform several graph computations including estimating the probability distribution after a random walk of length l, the mixing time, and other related quantities such as the conductance of the graph.