Structure and attribute index for approximate graph matching in large graphs

The increasing popularity of graph data in various domains has lead to a renewed interest in developing efficient graph matching techniques, especially for processing large graphs. In this paper, we study the problem of approximate graph matching in a large attributed graph. Given a large attributed...

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Bibliographic Details
Published inInformation systems (Oxford) Vol. 36; no. 6; pp. 958 - 972
Main Authors Zhu, Linhong, Keong Ng, Wee, Cheng, James
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.09.2011
Subjects
Approximate graph matching
Approximation
Construction
Graph indexing
Graph matching
Graphs
Indexing
Information retrieval
Information systems
Matching
Methods
Query processing
Similarity
Social network analysis
Graph indexing
Approximate graph matching
Social network analysis
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Summary:The increasing popularity of graph data in various domains has lead to a renewed interest in developing efficient graph matching techniques, especially for processing large graphs. In this paper, we study the problem of approximate graph matching in a large attributed graph. Given a large attributed graph and a query graph, we compute a subgraph of the large graph that best matches the query graph. We propose a novel structure-aware and attribute-aware index to process approximate graph matching in a large attributed graph. We first construct an index on the similarity of the attributed graph, by partitioning the large search space into smaller subgraphs based on structure similarity and attribute similarity. Then, we construct a connectivity-based index to give a concise representation of inter-partition connections. We use the index to find a set of best matching paths. From these best matching paths, we compute the best matching answer graph using a greedy algorithm. Experimental results on real datasets demonstrate the efficiency of both index construction and query processing. We also show that our approach attains high-quality query answers. ► A formal definition of approximate graph matching query problem. ► A structure-aware and attribute-aware indexing approach. ► An efficient tool for approximate graph matching query over massive real-life graphs. ► Extensive experimental study.
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ISSN:0306-4379
1873-6076
DOI:10.1016/j.is.2011.03.009