Topological Features and Iterative Node Elimination for Speeding up Subgraph Isomorphism Detection
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Author(s)
Dahm, Nicholas
Bunke, Horst
Caelli, Terry
Gao, Yongsheng
Griffith University Author(s)
Year published
2012
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In this paper we tackle the problem of subgraph isomorphism detection on large graphs, which may commonly be intractable, even with state of the art algorithms. Rather than competing with other matching algorithms, we define enhancements that can be used by (almost) any subgraph isomorphism algorithm, both current and future. These enhancements consist of a number of topological features to be added to the nodes, and a technique which we term "iterative node elimination". The fusion of these enhancements is shown to be able to reduce subgraph isomorphism matching times by a factor of over 4,500.In this paper we tackle the problem of subgraph isomorphism detection on large graphs, which may commonly be intractable, even with state of the art algorithms. Rather than competing with other matching algorithms, we define enhancements that can be used by (almost) any subgraph isomorphism algorithm, both current and future. These enhancements consist of a number of topological features to be added to the nodes, and a technique which we term "iterative node elimination". The fusion of these enhancements is shown to be able to reduce subgraph isomorphism matching times by a factor of over 4,500.
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Conference Title
2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012)
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Copyright Statement
© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subject
Pattern Recognition and Data Mining