RHOG: A Refinement-Operator Library for Directed Labeled Graphs
Santiago Onta\~n\'on
TL;DR
This paper introduces RHOG, a library for directed labeled graphs that offers core operations like subsumption, refinement, and similarity assessment, supported by foundational theory and practical implementation.
Contribution
It presents the foundational algorithms and implementation details of the RHOG library for graph refinement and comparison, filling a gap in tools for directed labeled graphs.
Findings
Provides a comprehensive library for graph refinement and comparison
Demonstrates the effectiveness of the library through practical applications
Supported by NSF funding
Abstract
This document provides the foundations behind the functionality provided by the G library (https://github.com/santiontanon/RHOG), focusing on the basic operations the library provides: subsumption, refinement of directed labeled graphs, and distance/similarity assessment between directed labeled graphs. G development was initially supported by the National Science Foundation, by the EAGER grant IIS-1551338.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Management and Algorithms · Data Mining Algorithms and Applications · Advanced Database Systems and Queries