Graph indexing: a frequent structure-based approach
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Substructure similarity search in graph databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Closure-Tree: An Index Structure for Graph Queries
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Fg-index: towards verification-free query processing on graph databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A novel spectral coding in a large graph database
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Taming verification hardness: an efficient algorithm for testing subgraph isomorphism
Proceedings of the VLDB Endowment
Efficient query processing on graph databases
ACM Transactions on Database Systems (TODS)
TALE: A Tool for Approximate Large Graph Matching
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Enhancing graph database indexing by suffix tree structure
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
iGraph: a framework for comparisons of disk-based graph indexing techniques
Proceedings of the VLDB Endowment
SAPPER: subgraph indexing and approximate matching in large graphs
Proceedings of the VLDB Endowment
CT-index: Fingerprint-based graph indexing combining cycles and trees
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
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Subgraph search in graph datasets is an important problem with numerous applications. Many feature-based indexing methods have been proposed for solving this problem. These methods have to index too many features or select some of them in order to get an index with good pruning capabilities. None of these directions can give an effective solution to all graph indexing issues. In this paper, we propose an efficient indexing approach which improves over current feature-based methods, neither by the costly feature selection nor by explicitly indexing a multitude of features. We achieve this by compressing multiple features into one feature with some neighborhood information encoded. Neighborhood is further used to prune unmatched feature occurrences between the query and data graphs, thus cutting down the search space of subgraph matching, which significantly reduce the verification cost. We implement the approach by exhaustively enumerating small paths as features. A novel path-at-a-time verification method that benefits from the occurrences pruning method is introduced. Via an extensive evaluation on both real and synthetic datasets, we show that our approach is effective and scalable, and outperforms state-of-the-art indexing methods.