Metric Indexing for Graph Similarity Search

Metric Indexing for Graph Similarity Search

Abstract

Finding the graphs that are most similar to a query graph in a large database is a common task with various applications. A widely used similarity measure is the graph edit distance, which provides an intuitive notion of similarity and naturally supports graphs with vertex and edge attributes. Since its computation is NP-hard, techniques for accelerating similarity search have been studied extensively. However, index-based approaches for this are almost exclusively designed for graphs with categorical vertex and edge labels and uniform edit costs. We propose a filter-verification framework for similarity search, which supports non-uniform edit costs for graphs with arbitrary attributes. We employ an expensive lower bound obtained by solving an optimal assignment problem. This filter distance satisfies the triangle inequality, making it suitable for acceleration by metric indexing. In subsequent stages, assignment-based upper bounds are used to avoid further exact distance computations. Our extensive experimental evaluation shows that a significant runtime advantage over both a linear scan and state-of-the-art methods is achieved.

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Authors
  • Bause, Franka
  • Blumenthal, David B.
  • Schubert, Erich
  • Kriege, Nils M.
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
14th International Conference on Similarity Search and Applications (SISAP)
Divisions
Data Mining and Machine Learning
Event Location
Dortmund, Virtual
Event Type
Conference
Event Dates
29.09.-01.10.2021
Series Name
Similarity Search and Applications
ISSN/ISBN
978-3-030-89656-0
Page Range
pp. 323-336
Date
29 September 2021
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