Subgraph isomorphism for biconnected outerplanar graphs in cubic time
Theoretical Computer Science
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
Journal of Algorithms
Enumerating all connected maximal common subgraphs in two graphs
Theoretical Computer Science
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Propositionalization approaches to relational data mining
Relational Data Mining
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
Cyclic pattern kernels for predictive graph mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and 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
GraphRank: Statistical Modeling and Mining of Significant Subgraphs in the Feature Space
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Comparison of descriptor spaces for chemical compound retrieval and classification
Knowledge and Information Systems
ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Patterns
Statistical Analysis and Data Mining
An Efficiently Computable Graph-Based Metric for the Classification of Small Molecules
DS '08 Proceedings of the 11th International Conference on Discovery Science
Deriving distance metrics from generality relations
Pattern Recognition Letters
Large-scale graph mining using backbone refinement classes
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Kernels For Structured Data
Frequent subgraph mining in outerplanar graphs
Data Mining and Knowledge Discovery
Effective feature construction by maximum common subgraph sampling
Machine Learning
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 Subtree Mining - An Overview
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
| Hi-index | 0.00 |
Metrics for structured data have received an increasing interest in the machine learning community. Graphs provide a natural representation for structured data, but a lot of operations on graphs are computationally intractable. In this article, we present a polynomial-time algorithm that computes a maximum common subgraph of two outerplanar graphs. The algorithm makes use of the block-and-bridge preserving subgraph isomorphism, which has significant efficiency benefits and is also motivated from a chemical perspective. We focus on the application of learning structure-activity relationships, where the task is to predict the chemical activity of molecules. We show how the algorithm can be used to construct a metric for structured data and we evaluate this metric and more generally also the block-and-bridge preserving matching operator on 60 molecular datasets, obtaining state-of-the-art results in terms of predictive performance and efficiency.