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tpaICDE18.pdf
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🔗 Source: datalab.snu.ac.kr
📊 Size: 2.67 MB
📄 Pages: 12 pages
⬇️ Downloads: 68

Summary

TPA is a fast, scalable, and accurate method for approximate random walk with restart (RWR) on large graphs, exploiting two properties: nodes close to a seed node are likely to be revisited, and RWR scores of distant nodes are proportional to their PageRank scores. TPA divides the problem into two subproblems, neighbor approximation and stranger approximation, conducted in the preprocessing and online phases, respectively. Experiments show TPA requires up to 3.5× less time and 40× less memory than state-of-the-art methods for preprocessing, and computes approximate RWR up to 30× faster in the online phase while maintaining high accuracy.

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Description

TPA is a fast, scalable, and accurate method for approximate random walk with restart (RWR) on large graphs, exploiting two properties: nodes close to a seed...

Technical Information

  • File Format: PDF
  • File Size: 2.67 MB
  • Pages: 12
  • Language: EN
  • Total Downloads: 68
  • Last Updated: 3 weeks ago

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