Finding top-k similar graphs in graph databases

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Conference Proceeding
ACM International Conference Proceeding Series, 2012, pp. 456 - 467
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Querying similar graphs in graph databases has been widely studied in graph query processing in recent years. Existing works mainly focus on subgraph similarity search and supergraph similarity search. In this paper, we study the problem of finding top-k graphs in a graph database that are most similar to a query graph. This problem has many applications, such as image retrieval and chemical compound structure search. Regarding the similarity measure, feature based and kernel based similarity measures have been used in the literature. But such measures are rough and may lose the connectivity information among substructures. In this paper, we introduce a new similarity measure based on the maximum common subgraph (MCS) of two graphs. We show that this measure can better capture the common and different structures of two graphs. Since computing the MCS of two graphs is NP-hard, we propose an algorithm to answer the top-k graph similarity query using two distance lower bounds with different computational costs, in order to reduce the number of MCS computations. We further introduce an indexing technique, which can better make use of the triangle property of similarities among graphs in the database to get tighter lower bounds. Three different indexing methods are proposed with different tradeoffs between pruning power and construction cost. We conducted extensive performance studies on large real datasets to evaluate the performance of our approaches. © 2012 ACM.
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