An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
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CloseGraph: mining closed frequent graph patterns
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Graph indexing: a frequent structure-based approach
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SPIN: mining maximal frequent subgraphs from graph databases
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
Closure-Tree: An Index Structure for Graph Queries
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Frequent subgraph pattern mining on uncertain graph data
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ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
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k-nearest neighbors in uncertain graphs
Proceedings of the VLDB Endowment
Mining frequent subgraphs over uncertain graph databases under probabilistic semantics
The VLDB Journal — The International Journal on Very Large Data Bases
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MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Mining frequent subgraph patterns in graph databases is a challenging and important problem with applications in several domains. Recently, there is a growing interest in generalizing the problem to uncertain graphs, which can model the inherent uncertainty in the data of many applications. The main difficulty in solving this problem results from the large number of candidate subgraph patterns to be examined and the large number of subgraph isomorphism tests required to find the graphs that contain a given pattern. The latter becomes even more challenging, when dealing with uncertain graphs. In this paper, we propose a method that uses an index of the uncertain graph database to reduce the number of comparisons needed to find frequent subgraph patterns. The proposed algorithm relies on the apriori property for enumerating candidate subgraph patterns efficiently. Then, the index is used to reduce the number of comparisons required for computing the expected support of each candidate pattern. It also enables additional optimizations with respect to scheduling and early termination, that further increase the efficiency of the method. The evaluation of our approach on three real-world datasets as well as on synthetic uncertain graph databases demonstrates the significant cost savings with respect to the state-of-the-art approach.