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Improvement of path analysis algorithm in social networks based on HBase

Author

Listed:
  • Yan Qiang

    (Taiyuan University of Technology)

  • Bo Pei

    (Taiyuan University of Technology)

  • Weili Wu

    (Taiyuan University of Technology
    University of Texas at Dallas)

  • Juanjuan Zhao

    (Taiyuan University of Technology)

  • Xiaolong Zhang

    (Taiyuan University of Technology
    Pennsylvania State University)

  • Yue Li

    (Taiyuan University of Technology)

  • Lidong Wu

    (University of Texas at Dallas)

Abstract

When social network has reached hundreds of million users, the analysis of data in social network services becomes very important. Understanding how nodes interconnect in large graphs is an essential problem in many fields. In order to find connecting nodes between two nodes or two groups of source nodes in huge graphs, we propose a parallelized data-mining algorithm to get the shortest path between nodes in a social network based on HBase distributed key/value store. Our algorithm can achieve the shortest path among different nodes in network under the parallel environment. We analyze the social network model by this algorithm first, and then optimize the output from cloud platform by using the intermediary degrees and degree central algorithm. Finally, with a simulated social network, we validate the efficiency of the proposed algorithm. The experiment results indicate that our algorithm can improve the efficiency of parallel breath-first search (BSF).

Suggested Citation

  • Yan Qiang & Bo Pei & Weili Wu & Juanjuan Zhao & Xiaolong Zhang & Yue Li & Lidong Wu, 2014. "Improvement of path analysis algorithm in social networks based on HBase," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 588-599, October.
    • Handle: RePEc:spr:jcomop:v:28:y:2014:i:3:d:10.1007_s10878-013-9675-z
    DOI: 10.1007/s10878-013-9675-z
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    References listed on IDEAS

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    1. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
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