Graph Indexing: A Frequent Structure-based Approach

Graph has become increasingly important in modelling complicated structures and schemaless data such as proteins, chemical compounds, and XML documents. Given a graph query, it is desirable to retrieve graphs quickly from a large database via graph-based indices. In this paper, we investigate the issues of indexing graphs and propose a novel solution by applying a graph mining technique. Different from the existing path-based methods, our approach, called gIndex, makes use of frequent substructure as the basic indexing feature. Frequent substructures are ideal candidates since they explore the intrinsic characteristics of the data and are relatively stable to database updates. To reduce the size of index structure, two techniques, size-increasing support constraint and discriminative fragments, are introduced. Our performance study shows that gIndex has 10 times smaller index size, but achieves 3?10 times better performance in comparison with a typical path-based method, GraphGrep. Th...

• Type: Conference
• Year: 2004
• Where: SIGMOD
• Authors: Xifeng Yan, Philip S. Yu, Jiawei Han
• Keywords: Basic Indexing Feature, Database, Graph Indexing Problem, Graph Mining Technique, SIGMOD 2004