NDLI: Compressed feature-based filtering and verification approach for subgraph search

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Compressed feature-based filtering and verification approach for subgraph search

Content Provider	ACM Digital Library
Author	Gouda, Karam Hassaan, Mosab
Abstract	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.
Starting Page	287
Ending Page	298
Page Count	12
File Format	PDF
ISBN	9781450315975
DOI	10.1145/2452376.2452411
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-03-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Subgraph query processing Feature-based indexing Verification methods
Content Type	Text
Resource Type	Article

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