CT-index: Fingerprint-based graph indexing combining cycles and trees

Fast-On*: An Extended Algorithm for Graph Isomorphism Problem and Graph Query Processing

“…Given a query graph q and a graph database } ,..., , , { = Figure 1, Graph G should be returned as the result since G contains q. There are many algorithms have been proposed for subgraph search problem, such as [6,22,3,21,10,23,9,26,17,29,11].…”

“…We evaluate Fast-ON * and compare it with Ullman and Vflib on real and synthetic datasets for GIP, with SMS [27] and GADDI [24] for SLG, and with CT-index [11] and FG-index [3] for SSP.…”

“…The major challenge in this scenario is to reduce the number of pairwise subgraph isomorphism checking. A number of graph indexing techniques have been proposed to address this challenge [22,3,23,26,17,11,10,29]. There are two categories of graph indexing techniques: feature-based index and nonfeature-based index.…”

“…There are two categories of graph indexing techniques: feature-based index and nonfeature-based index. In feature-based index [22,3,23,26,17,11], some subgraphs are chosen as index features, and an inverted list is built for each feature. Generally, query processing follows the filtering-andverification framework.…”

“…Graph query processing [6,22,3,21,10,23,9,26,17,29,11,24,28,27,25] has attracted much attention in recent years thanks to the increasing popularity of graph databases in various application domains. Existing research on graph query processing is conducted mainly on two types of graph databases as follows.…”