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

Aaron Smalter, Jun Huan, Yi Jia, Gerald Lushington
2010 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
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
more » ... s 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.
doi:10.1109/tcbb.2009.80 pmid:20431140 pmcid:PMC3058227 fatcat:z5dysutf45delodcbpzeiijqhe