ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds
ICDM '03 Proceedings of the Third IEEE International Conference on 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
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
| Hi-index | 0.00 |
The article proposes a generic method to classify vertices or edges of a labeled graph. More precisely the method computes a confidence index for each vertex v or edge e to be a member of a target class by mining the topological environments of v or e. The method contributes to knowledge discovery since it exhibits for each edge or vertex an informative environnement that explains the found confidence. When applied to the problem of discovering strategic bonds in molecules, the method correctly classifies most of the bonds while providing relevant explanations to chemists. The developed algorithm GemsBond outperforms both speed and scalability of the learning method that has previously been applied to the same application while giving similar results.