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G-Hash: Towards Fast Kernel-Based Similarity Search in Large Graph Databases

G-Hash: Towards Fast Kernel-Based Similarity Search in Large Graph Databases

Xiaohong Wang, Jun Huan, Aaron Smalter, Gerald H. Lushington
Copyright: © 2012 |Pages: 38
ISBN13: 9781613500538|ISBN10: 161350053X|EISBN13: 9781613500545
DOI: 10.4018/978-1-61350-053-8.ch008
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MLA

Wang, Xiaohong, et al. "G-Hash: Towards Fast Kernel-Based Similarity Search in Large Graph Databases." Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, IGI Global, 2012, pp. 176-213. https://doi.org/10.4018/978-1-61350-053-8.ch008

APA

Wang, X., Huan, J., Smalter, A., & Lushington, G. H. (2012). G-Hash: Towards Fast Kernel-Based Similarity Search in Large Graph Databases. In S. Sakr & E. Pardede (Eds.), Graph Data Management: Techniques and Applications (pp. 176-213). IGI Global. https://doi.org/10.4018/978-1-61350-053-8.ch008

Chicago

Wang, Xiaohong, et al. "G-Hash: Towards Fast Kernel-Based Similarity Search in Large Graph Databases." In Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, 176-213. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-053-8.ch008

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Abstract

Our objective in this chapter is to enable fast similarity search in large graph databases with graph kernel functions. In particular, we propose (i) a novel kernel-based similarity measurement and (ii) an efficient indexing structure for graph data management. In our method, we use a hash table to support efficient storage and fast search of the extracted local features from graph data. Using the hash table, we have developed a graph kernel function to capture the intrinsic similarity of graphs and for fast similarity query processing. We have demonstrated the utility of the proposed methods using large chemical structure graph databases.

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