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HN-Sim: A Structural Similarity Measure over Object-Behavior Networks

  • Jiazhen Nian
  • Shanshan Wang
  • Yan Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8346)

Abstract

Measurement of similarity is a critical work for many applications such as text analysis, link prediction and recommendation. However, existing work stresses on content and rarely involves structural features. Even fewer methods are applicable for heterogeneous network, which is prevalent in the real world, such as bibliographic information network. To address this problem, we propose a new measurement of similarity from the perspective of the heterogeneous structure. Heterogeneous neighborhood is utilized to instantiate the topological features and categorize the related nodes in graph model. We make a comparison between our measurement and some traditional ones with the real data in DBLP and Flickr. Manual evaluation shows that our method outperforms the traditional ones.

Keywords

Structural similarity measurement Heterogeneous network Object-behavior network 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jiazhen Nian
    • 1
  • Shanshan Wang
    • 1
  • Yan Zhang
    • 1
  1. 1.Department of Machine IntelligencePeking University Key Laboratory on Machine Perception, Ministry of EducationBeijingP.R. China

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