C4.5: programs for machine learning
C4.5: programs for machine learning
Kernel principal component analysis
Advances in kernel methods
Algorithmics and applications of tree and graph searching
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third 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
SPIN: mining maximal frequent subgraphs from graph databases
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
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Graph indexing based on discriminative frequent structure analysis
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2004
2005 Speical Issue: Graph kernels for chemical informatics
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Frequent subgraph mining in outerplanar graphs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Coherent closed quasi-clique discovery from large dense graph databases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph Classification Using Genetic Algorithm and Graph Probing Application to Symbol Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Correlation search in graph databases
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast direction-aware proximity for graph mining
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Comparison of descriptor spaces for chemical compound retrieval and classification
Knowledge and Information Systems
Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
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Graph data mining is an active research area. Graphs are general modeling tools to organize information from heterogeneous sources and have been applied in many scientific, engineering, and business fields. With the fast accumulation of graph data, building highly accurate predictive models for graph data emerges as a new challenge that has not been fully explored in the data mining community. In this paper, we demonstrate a novel technique called graph pattern diffusion (GPD) kernel. Our idea is to leverage existing frequent pattern discovery methods and to explore the application of kernel classifier (e.g., support vector machine) in building highly accurate graph classification. In our method, we first identify all frequent patterns from a graph database. We then map subgraphs to graphs in the graph database and use a process we call "pattern diffusion” to label nodes in the graphs. Finally, we designed a graph alignment algorithm to compute the inner product of two graphs. We have tested our algorithm using a number of chemical structure data. The experimental results demonstrate that our method is significantly better than competing methods such as those kernel functions based on paths, cycles, and subgraphs.