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    GPD: A Graph Pattern Diffusion Kernel for Accurate Graph Classification with Applications in Cheminformatics

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    Issue Date
    2010-04
    Author
    Smalter Hall, Aaron
    Huan, Luke
    Jia, Yi
    Lushington, Gerald H.
    Publisher
    Institute of Electrical and Electronics Engineers
    Type
    Article
    Article Version
    Scholarly/refereed, author accepted manuscript
    Published Version
    http://ieeexplore.ieee.org/document/5313793/
    Rights
    (c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
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    Abstract
    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.
    URI
    http://hdl.handle.net/1808/23569
    DOI
    https://doi.org/10.1109/TCBB.2009.80
    Collections
    • Electrical Engineering and Computer Science Scholarly Works [301]
    Citation
    Smalter, Aaron, Jun Huan, Yi Jia, and Gerald Lushington. "GPD: A Graph Pattern Diffusion Kernel for Accurate Graph Classification with Applications in Cheminformatics." IEEE/ACM Transactions on Computational Biology and Bioinformatics 7.2 (2010): 197-207.

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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