A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
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A graph distance metric combining maximum common subgraph and minimum common supergraph
Pattern Recognition Letters
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The Journal of Machine Learning Research
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
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GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
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Pattern Recognition Letters
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Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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This paper proposes two efficient kernels for comparing acyclic, directed graphs. The first kernel counts the number of common paths and allows for weighing according to path-length and/or according to the vertices contained in each particular path. The second kernel counts the number of paths in common minors of the graphs involved and allows for length- and vertex-weighting too. Both kernels have algorithmic complexity that is cubic in the size of the vertex-set. The performance of the algorithms is concisely demonstrated using synthetic and real data.