Refinement-Based Similarity Measures for Directed Labeled Graphs

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9969)


This paper presents a collection of similarity measures based on refinement operators for directed labeled graphs (DLGs). We build upon previous work on refinement operators for other representation formalisms such as feature terms and description logics. Specifically, we present refinement operators for DLGs, which enable the adaptation of three similarity measures to DLGs: the anti-unification-based, \(S_{\lambda }\), the property-based, \(S_{\pi }\), and the weighted property-based, \(S_{w\pi }\), similarities. We evaluate the resulting measures empirically comparing them to existing similarity measures for structured data.


Similarity assessment Refinement operators Labeled graphs 



This research was supported by grant IIS-1551338 from the National Science Foundation (NSF).


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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Computer Science DepartmentDrexel UniversityPhiladelphiaUSA

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