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PageRank on an evolving graph

Published: 12 August 2012 Publication History

Abstract

One of the most important features of the Web graph and social networks is that they are constantly evolving. The classical computational paradigm, which assumes a fixed data set as an input to an algorithm that terminates, is inadequate for such settings. In this paper we study the problem of computing PageRank on an evolving graph. We propose an algorithm that, at any moment in the time and by crawling a small portion of the graph, provides an estimate of the PageRank that is close to the true PageRank of the graph at that moment. We will also evaluate our algorithm experimentally on real data sets and on randomly generated inputs. Under a stylized model of graph evolution, we show that our algorithm achieves a provable performance guarantee that is significantly better than the naive algorithm that crawls the nodes in a round-robin fashion.

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    cover image ACM Conferences
    KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2012
    1616 pages
    ISBN:9781450314626
    DOI:10.1145/2339530
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 12 August 2012

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    Author Tags

    1. dynamic graphs
    2. pagerank
    3. random walks

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    • (2025)Quantum social network analysis: Methodology, implementation, challenges, and future directionsInformation Fusion10.1016/j.inffus.2024.102808117(102808)Online publication date: May-2025
    • (2025)Weak dangling block reordering and multi-step block compression for efficiently computing and updating PageRank solutionsJournal of Computational and Applied Mathematics10.1016/j.cam.2024.116332458(116332)Online publication date: Apr-2025
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