Fast generalized Fourier transforms
Theoretical Computer Science
The efficient computation of Fourier transforms on the symmetric group
Mathematics of Computation
Protein function prediction via graph kernels
Bioinformatics
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Entire regularization paths for graph data
Proceedings of the 24th international conference on Machine learning
Graph kernels between point clouds
Proceedings of the 25th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
Boosting with structure information in the functional space: an application to graph classification
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A Fourier space algorithm for solving quadratic assignment problems
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Information-geometric graph indexing from bags of partial node coverages
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Weisfeiler-Lehman Graph Kernels
The Journal of Machine Learning Research
Effective graph classification based on topological and label attributes
Statistical Analysis and Data Mining
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Current graph kernels suffer from two limitations: graph kernels based on counting particular types of subgraphs ignore the relative position of these subgraphs to each other, while graph kernels based on algebraic methods are limited to graphs without node labels. In this paper we present the graphlet spectrum, a system of graph invariants derived by means of group representation theory that capture information about the number as well as the position of labeled subgraphs in a given graph. In our experimental evaluation the graphlet spectrum outperforms state-of-the-art graph kernels.