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1818 results in Knowledge Management, Databases and Data Mining

Page 46 of 91

  • 5 - Link Analysis

  • Jure Leskovec, Stanford University, California, Anand Rajaraman, Jeffrey David Ullman, Stanford University, California
  • Book:
    Mining of Massive Datasets
    Published online:
    16 April 2020
    Print publication:
    09 January 2020, pp 169-205

  • Summary

    We shall begin the chapter by explaining what PageRank is and how it is computed efficiently. Yet the war between those who want to make the Web useful and those who would exploit it for their own purposes is never over. When PageRank was established as an essential technique for a search engine, spammers invented ways to manipulate the PageRank of a Web page, often called link spam. That development led to the response of TrustRank and other techniques for preventing spammers from attacking PageRank. We shall discuss TrustRank and other approaches to detecting link spam. Finally, this chapter also covers some variations on PageRank. These techniques include topic-sensitive PageRank (which can also be adapted for combating link spam) and the HITS, or “hubs and authorities” approach to evaluating pages on the Web.

  • 2 - MapReduce and the New Software Stack

  • Jure Leskovec, Stanford University, California, Anand Rajaraman, Jeffrey David Ullman, Stanford University, California
  • Book:
    Mining of Massive Datasets
    Published online:
    16 April 2020
    Print publication:
    09 January 2020, pp 20-77

  • Summary

    We include in this chapter a discussion of generalizations of MapReduce, first to systems that support acyclic workflows and then to systems that implement recursive algorithms. Our last topic for this chapter is the design of good MapReduce algorithms, a subject that often differs significantly from the matter of designing good parallel algorithms to be run on a supercomputer. When designing MapReduce algorithms, we often find that the greatest cost is in the communication. We thus investigate communication cost and what it tells us about the most efficient MapReduce algorithms. For several common applications of MapReduce we are able to give families of algorithms that optimally trade the communication cost against the degree of parallelism.

Page 46 of 91