TALE: A Tool for Approximate Large Graph Matching

Abstract-- Large graph datasets are common in many emerging database applications, and most notably in large-scale scientific applications. To fully exploit the wealth of information encoded in graphs, effective and efficient graph matching tools are critical. Due to the noisy and incomplete nature of real graph datasets, approximate, rather than exact, graph matching is required. Furthermore, many modern applications need to query large graphs, each of which has hundreds to thousands of nodes and edges. This paper presents a novel technique for approximate matching of large graph queries. We propose a novel indexing method that incorporates graph structural information in a hybrid index structure. This indexing technique achieves high pruning power and the index size scales linearly with the database size. In addition, we propose an innovative matching paradigm to query large graphs. This technique distinguishes nodes by their importance in the graph structure. The matching algorithm ...

• Type: Conference
• Year: 2008
• Where: ICDE
• Authors: Yuanyuan Tian, Jignesh M. Patel
• Keywords: Database, Efficient Graph Matching, ICDE 2008, Large Graph Datasets, Real Graph Datasets