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Fast Graph Similarity Search via Locality Sensitive Hashing

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Advances in Multimedia Information Processing -- PCM 2015 (PCM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9314))

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Abstract

Similarity search in graph databases has been widely studied in graph query processing in recent years. With the fast accumulation of graph databases, it is worthwhile to develop a fast algorithm to support similarity search in large-scale graph databases. In this paper, we study k-NN similarity search problem via locality sensitive hashing. We propose a fast graph search algorithm, which first transforms complex graphs into vectorial representations based on the prototypes in the database and then accelerates query efficiency in Euclidean space by employing locality sensitive hashing. Additionally, a general retrieval framework is established in our approach. Experiments on three real datasets show that our work achieves high performance both on the accuracy and the efficiency of the presented algorithm.

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  1. 1.

    http://pubchem.ncbi.nlm.nih.gov/.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (61370125 and 61402026), SKLSDE-2014ZX-07 and SKLSDE-2015ZX-04.

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Authors and Affiliations

  1. State Key Lab of Software Development Environment, Beihang University, Beijing, China

    Boyu Zhang, Xianglong Liu & Bo Lang

Authors
  1. Boyu Zhang
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  2. Xianglong Liu
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  3. Bo Lang
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Corresponding author

Correspondence to Xianglong Liu .

Editor information

Editors and Affiliations

  1. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Yo-Sung Ho

  2. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Jitao Sang

  3. ICU, IVY Lab, KAIST, Daejeon, Korea (Republic of)

    Yong Man Ro

  4. KAIST, Daejeon, Korea (Republic of)

    Junmo Kim

  5. College of Computer Science, Zhejiang University, Hangzhou, China

    Fei Wu

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Zhang, B., Liu, X., Lang, B. (2015). Fast Graph Similarity Search via Locality Sensitive Hashing. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_60

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  • DOI: https://doi.org/10.1007/978-3-319-24075-6_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24074-9

  • Online ISBN: 978-3-319-24075-6

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