Fast generalized Fourier transforms
Theoretical Computer Science
Triple correlation on groups
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Protein function prediction via graph kernels
Bioinformatics
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The Journal of Machine Learning Research
A Fourier space algorithm for solving quadratic assignment problems
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Weisfeiler-Lehman Graph Kernels
The Journal of Machine Learning Research
A new protein graph model for function prediction
Computational Biology and Chemistry
Algebraic geometric comparison of probability distributions
The Journal of Machine Learning Research
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The central issue in representing graph-structured data instances in learning algorithms is designing features which are invariant to permuting the numbering of the vertices. We present a new system of invariant graph features which we call the skew spectrum of graphs. The skew spectrum is based on mapping the adjacency matrix of any (weigted, directed, unlabeled) graph to a function on the symmetric group and computing bispectral invariants. The reduced form of the skew spectrum is computable in O(n3) time, and experiments show that on several benchmark datasets it can outperform state of the art graph kernels.