On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Frequent subgraph mining in outerplanar graphs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Don't be afraid of simpler patterns
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Frequent subgraph mining in outerplanar graphs
Data Mining and Knowledge Discovery
Graph-based data mining for biological applications
AI Communications
Annals of Mathematics and Artificial Intelligence
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In machine learning, there has been an increased interest in metrics on structured data. The application we focus on is drug discovery. Although graphs have become very popular for the representation of molecules, a lot of operations on graphs are NP-complete. Representing the molecules as outerplanar graphs, a subclass within general graphs, and using the block-and-bridge preserving subgraph isomorphism, we define a metric and we present an algorithm for computing it in polynomial time. We evaluate this metric and more generally also the block-and-bridge preserving matching operator on a large dataset of molecules, obtaining favorable results.