Graph classification based on optimizing graph spectra
DS'10 Proceedings of the 13th international conference on Discovery science
Neighborhood hash graph kernel for protein-protein interaction extraction
Journal of Biomedical Informatics
Weisfeiler-Lehman Graph Kernels
The Journal of Machine Learning Research
Characterizing the roles of classes and their fault-proneness through change metrics
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
Efficient graph kernels by randomization
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Structural detection of android malware using embedded call graphs
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
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The design of a good kernel is fundamental for knowledge discovery from graph-structured data. Existing graph kernels exploit only limited information about the graph structures but are still computationally expensive. We propose a novel graph kernel based on the structural characteristics of graphs. The key is to represent node labels as binary arrays and characterize each node using logical operations on the label set of the connected nodes. Our kernel has a linear time complexity with respect to the number of nodes times the average number of neighboring nodes in the given graphs. The experimental result shows that the proposed kernel performs comparable and much faster than a state-of-the-art graph kernel for benchmark data sets and shows high scalability for new applications with large graphs.