Experiments in Predicting Biodegradability
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Inference for the Generalization Error
Machine Learning
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Semi-supervised protein classification using cluster kernels
Bioinformatics
Weighted decomposition kernels
ICML '05 Proceedings of the 22nd international conference on Machine learning
ChemDB update—full-text search and virtual chemical space
Bioinformatics
The Journal of Machine Learning Research
Online structural graph clustering using frequent subgraph mining
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Adapted transfer of distance measures for quantitative structure-activity relationships
DS'10 Proceedings of the 13th international conference on Discovery science
Parallel structural graph clustering
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
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In recent years, graph kernels have received considerable interest within the machine learning and data mining community. Here, we introduce a novel approach enabling kernel methods to utilize additional information hidden in the structural neighborhood of the graphs under consideration. Our novel structural cluster kernel (SCK) incorporates similarities induced by a structural clustering algorithm to improve state-of-the-art graph kernels. The approach taken is based on the idea that graph similarity can not only be described by the similarity between the graphs themselves, but also by the similarity they possess with respect to their structural neighborhood. We applied our novel kernel in a supervised and a semi-supervised setting to regression and classification problems on a number of real-world datasets of molecular graphs. Our results show that the structural cluster similarity information can indeed leverage the prediction performance of the base kernel, particularly when the dataset is structurally sparse and consequently structurally diverse. By additionally taking into account a large number of unlabeled instances the performance of the structural cluster kernel can further be improved.