Loading...
GPD: A Graph Pattern Diffusion Kernel for Accurate Graph Classification with Applications in Cheminformatics
Smalter Hall, Aaron Huan, Luke Jia, Yi Lushington, Gerald H.
Smalter Hall, Aaron
Huan, Luke
Jia, Yi
Lushington, Gerald H.
Citations
Altmetric:
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.
Description
Date
2010-04
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers
Files
Loading...
huan_graph.pdf
Adobe PDF, 956.32 KB
Research Projects
Organizational Units
Journal Issue
Keywords
Graph classification, Graph alignment, Frequent subgraph mining
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.