Efficient processing of graph similarity queries with edit distance constraints

  • Authors:

  • Xiang Zhao;Chuan Xiao;Xuemin Lin;Wei Wang;Yoshiharu Ishikawa

  • Affiliations:

  • The University of New South Wales, Sydney, Australia and NICTA, Sydney, Australia;Nagoya University, Nagoya, Japan;The University of New South Wales, Sydney, Australia;The University of New South Wales, Sydney, Australia;Nagoya University, Nagoya, Japan

  • Venue:
  • The VLDB Journal — The International Journal on Very Large Data Bases
  • Year:
  • 2013

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Abstract

Graphs are widely used to model complicated data semantics in many applications in bioinformatics, chemistry, social networks, pattern recognition, etc. A recent trend is to tolerate noise arising from various sources such as erroneous data entries and find similarity matches. In this paper, we study graph similarity queries with edit distance constraints. Inspired by the $$q$$-gram idea for string similarity problems, our solution extracts paths from graphs as features for indexing. We establish a lower bound of common features to generate candidates. Efficient algorithms are proposed to handle three types of graph similarity queries by exploiting both matching and mismatching features as well as degree information to improve the filtering and verification on candidates. We demonstrate the proposed algorithms significantly outperform existing approaches with extensive experiments on real and synthetic datasets.