Issue No. 03 - March (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.28
Guoren Wang , Northeastern University, Shenyang
Bin Wang , Northeastern University, Shenyang
Xiaochun Yang , Northeastern University, Shenyang
Ge Yu , Northeastern University, Shenyang
The graph structure is a very important means to model schemaless data with complicated structures, such as protein-protein interaction networks, chemical compounds, knowledge query inferring systems, and road networks. This paper focuses on the index structure for similarity search on a set of large sparse graphs and proposes an efficient indexing mechanism by introducing the Q-Gram idea. By decomposing graphs to small grams (organized by κ-Adjacent Tree patterns) and pairing-up on those κ-Adjacent Tree patterns, the lower bound estimation of their edit distance can be calculated for candidate filtering. Furthermore, we have developed a series of techniques for inverted index construction and online query processing. By building the candidate set for the query graph before the exact edit distance calculation, the number of graphs need to proceed into exact matching can be greatly reduced. Extensive experiments on real and synthetic data sets have been conducted to show the effectiveness and efficiency of the proposed indexing mechanism.
Graph indexing, similarity search, \kappa-adjacent tree.
G. Yu, B. Wang, G. Wang and X. Yang, "Efficiently Indexing Large Sparse Graphs for Similarity Search," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 440-451, 2010.