Closure-Tree: An Index Structure for Graph Queries

By Huahai He, Ambuj K. Singh. IEEE International Conference on Data Engineering, 2006

349 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

TALE: A Tool for Approximate Large Graph Matching (2008, methodology): “The database community has had a long-standing interest in querying graph databases [6], [9], [13], [15], [17]–[22].”
Graphs-at-a-time: query language and access methods for graph databases (2008, methodology): “Recent graph indexing techniques [7, 15, 19, 29, 31, 34, 35, 36, 37] or multi-dimensional indexing methods such as R-trees can be used for this purpose.”
Taming verification hardness: an efficient algorithm for testing subgraph isomorphism (2008, methodology): “More recently, He and Singh propose a clustering-based index called Closure-tree to support both subgraph queries and similarity queries [9].”
Comparing Stars: On Approximating Graph Edit Distance (2009, methodology): “Because of the technical limitation of processing graph search using conventional database technologies, enormous eﬀorts [26, 13, 35, 9, 37] have been put into constructing practical graph searching methods.”
GADDI: distance index based subgraph matching in biological networks (2009, background): “In Closure-Tree [17], a pseudo subgraph isomorphism test is performed by checking the existence of a semi-perfect match from vertices in the query graph to vertices in a data graph (or graph closure).”