The nature of statistical learning theory
The nature of statistical learning theory
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Templates for the solution of algebraic eigenvalue problems: a practical guide
Templates for the solution of algebraic eigenvalue problems: a practical guide
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Cyclic pattern kernels for predictive graph mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and 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
Extensions of marginalized graph kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning the Topological Properties of Brain Tumors
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
2005 Speical Issue: Graph kernels for chemical informatics
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Graph kernels based on tree patterns for molecules
Machine Learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Predicting structured objects with support vector machines
Communications of the ACM - Scratch Programming for All
ECM-aware cell-graph mining for bone tissue modeling and classification
Data Mining and Knowledge Discovery
Networks: An Introduction
The Journal of Machine Learning Research
Discriminative frequent subgraph mining with optimality guarantees
Statistical Analysis and Data Mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Centrality measures based on current flow
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
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Graph classification is an important data mining task, and various graph kernel methods have been proposed recently for this task. These methods have proven to be effective, but they tend to have high computational overhead. In this paper, we propose an alternative approach to graph classification that is based on feature vectors constructed from different global topological attributes, as well as global label features. The main idea is that the graphs from the same class should have similar topological and label attributes. Our method is simple and easy to implement, and via a detailed comparison on real benchmark datasets, we show that our topological and label feature-based approach delivers competitive classification accuracy, with significantly better results on those datasets that have large unlabeled graph instances. Our method is also substantially faster than most other graph kernels. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012 © 2012 Wiley Periodicals, Inc.