IEEE Transactions on Knowledge and Data Engineering
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
Theoretical Computer Science
ICML '06 Proceedings of the 23rd international conference on Machine learning
Kernels for Chemical Compounds in Biological Screening
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Kernel Methods for Graphs: A Comprehensive Approach
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Marginalized multi-instance kernels
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A graph matching method and a graph matching distance based on subgraph assignments
Pattern Recognition Letters
Image classification using marginalized kernels for graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Kernel fusion for image classification using fuzzy structural information
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Learning with kernels and logical representations
Probabilistic inductive logic programming
IEEE Computational Intelligence Magazine
The Journal of Machine Learning Research
Characterizing structural relationships in scenes using graph kernels
ACM SIGGRAPH 2011 papers
Learning graph prototypes for shape recognition
Computer Vision and Image Understanding
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Graph characterization via backtrackless paths
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Weisfeiler-Lehman Graph Kernels
The Journal of Machine Learning Research
Inferring a graph from path frequency
CPM'05 Proceedings of the 16th annual conference on Combinatorial Pattern Matching
Computational and statistical methods in bioinformatics
AM'03 Proceedings of the Second international conference on Active Mining
Inferring a graph from path frequency
Discrete Applied Mathematics
Effective graph classification based on topological and label attributes
Statistical Analysis and Data Mining
Pattern Recognition Letters
Entity disambiguation in anonymized graphs using graph kernels
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Structured representations in a content based image retrieval context
Journal of Visual Communication and Image Representation
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Positive definite kernels between labeled graphs have recently been proposed. They enable the application of kernel methods, such as support vector machines, to the analysis and classification of graphs, for example, chemical compounds. These graph kernels are obtained by marginalizing a kernel between paths with respect to a random walk model on the graph vertices along the edges. We propose two extensions of these graph kernels, with the double goal to reduce their computation time and increase their relevance as measure of similarity between graphs. First, we propose to modify the label of each vertex by automatically adding information about its environment with the use of the Morgan algorithm. Second, we suggest a modification of the random walk model to prevent the walk from coming back to a vertex that was just visited. These extensions are then tested on benchmark experiments of chemical compounds classification, with promising results.