G-hash: towards fast kernel-based similarity search in large graph databases

Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important research topic. Graph similarity search has applications in a wide range of domains including cheminformatics, bioinformatics, sensor network management, social network management, and XML documents, among others. Most of the current graph indexing methods focus on subgraph query processing, i.e. determining the set of database graphs that contains the query graph and hence do not directly support similarity search. In data mining and machine learning, various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models for supervised learning, graph kernel functions have (i) hig...

• Type: Conference
• Year: 2009
• Where: EDBT
• Authors: Xiaohong Wang, Aaron M. Smalter, Jun Huan, Gerald H. Lushington
• Keywords: EDBT 2009, Graph Databases, Graph Kernel Function, Similarity Search