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A Weighted Similarity Measure Based on Meta Structure in Heterogeneous Information Networks

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Knowledge Management and Acquisition for Intelligent Systems (PKAW 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11016))

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Abstract

Evaluating the similarity between two objects in heterogeneous information network is a significant part of information science. The existing meta-structure based similarity measures only consider one meta-structure, which leads to a loss of accuracy. Based on the meta-structure, this paper proposes a weighted method to tackle the problem. We put forward a weighting algorithm that determines the value of weight to each meta-structure according to the set of the user’s preferences, and to compute the similarity value, we convert meta-structure into meta-path and use a novel meta-path based similarity measure StruSim. The top-k similarity research experiment is conducted to prove the effectiveness of the novel method. Using the measure nDCG, we conclude that StruSim performs better than PathSim, HeteSim, and AvgSim. And the multiple meta-structure methods are better than BSCSE and unweighted meta-path based methods. At last, we propose an interpolation and derivation method to search the optimal bias factor in StruSim to achieve a better performance.

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Author information

Authors and Affiliations

  1. College of Mathematics and Physics, China University of Geosciences, Wuhan, 430074, China

    Zhaochen Li

  2. School of Mathematical Sciences, Peking University, Beijing, 100871, China

    Hengliang Wang

Authors
  1. Zhaochen Li
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  2. Hengliang Wang
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Corresponding author

Correspondence to Zhaochen Li .

Editor information

Editors and Affiliations

  1. University of Tsukuba , Tokyo, Japan

    Kenichi Yoshida

  2. Shih Chien University, Taipei City, Taiwan

    Maria Lee

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Li, Z., Wang, H. (2018). A Weighted Similarity Measure Based on Meta Structure in Heterogeneous Information Networks. In: Yoshida, K., Lee, M. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2018. Lecture Notes in Computer Science(), vol 11016. Springer, Cham. https://doi.org/10.1007/978-3-319-97289-3_22

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  • DOI: https://doi.org/10.1007/978-3-319-97289-3_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97288-6

  • Online ISBN: 978-3-319-97289-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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