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A Parallel Method for All-Pair SimRank Similarity Computation

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Algorithms and Architectures for Parallel Processing (ICA3PP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11334))

Abstract

How to measure SimRank similarity of all-pair vertices in a graph is a very important research topic which has a wide range of applications in many fields. However, computation of SimRank is costly in both time and space, making traditional computing methods failing to handle graph data of ever-growing size.

This paper proposes a parallel multi-level solution for all-pair SimRank similarity computing on large graphs. We partition the objective graph first with the idea of modularity maximization and get a collapsed graph based on the blocks. Then we compute the similarities between verteices inside a block as well as the similarities between the blocks. In the end, we integrate these two types of similarities and calculate the approximate SimRank simlarities between all vertex pairs. The method is implemented on Spark platform and it makes an improvement on time efficiency while maintaining the effectiveness compared to SimRank.

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Notes

  1. 1.

    https://snap.stanford.edu/data/wiki-topcats.html.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Nanjing University, Nanjing, 210023, China

    Xuan Huang, Xingkun Gao, Jie Tang & Gangshan Wu

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Xuan Huang, Xingkun Gao, Jie Tang & Gangshan Wu

Authors
  1. Xuan Huang
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  2. Xingkun Gao
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  3. Jie Tang
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  4. Gangshan Wu
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Corresponding author

Correspondence to Jie Tang .

Editor information

Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Guangzhou University, Guangzhou, China

    Jin Li

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Huang, X., Gao, X., Tang, J., Wu, G. (2018). A Parallel Method for All-Pair SimRank Similarity Computation. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11334. Springer, Cham. https://doi.org/10.1007/978-3-030-05051-1_41

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  • DOI: https://doi.org/10.1007/978-3-030-05051-1_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05050-4

  • Online ISBN: 978-3-030-05051-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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