Earticle

초록

영어

Based on the study about the basic idea of PageRank algorithm, combining with the MapReduce distributed programming concepts, the paper first proposed a parallel PageRank algorithm based on adjacency list which is suitable for massive data processing. Then, after examining the essential characteristics of iteration hidden behind the PageRank, it provided an iteration acceleration model based on vector computing. Following, using such acceleration model, the paper again brought forward a parallel PageRank algorithm with power iteration acceleration with MapReduce. Finally, after abundant experimental analyses, it has been proved that the both the two proposed algorithm can be suitable for massive data processing and the 2nd one can significantly reduce the numbers of iteration and improve the efficiency of PageRank algorithm.

목차

Abstract
 1. Introduction
 2. PageRank Survey
  2.1. PageRank Introduction
  2.2. PageRank Research Statuses
 3. PageRank Parallel Algorithm based Power Method
 4. Parallel PageRank Algorithm Based on Power Iteration Acceleration
  4.1. Power Iteration Acceleration
  4.2. Parallel PageRank Algorithm Based On Power Iteration Acceleration
 5. The Discussion of Experimental Results
  5.1. Experimental Scheme Design
  5.2. Experimental Results Analysis
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Chun Liu [ School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China ]
• Yuqiang Li [ School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605