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- W2271661574 abstract "Graph mining means a series of processes for finding frequent sub-graphs in graph databases with complex structures. To obtain useful sub-graphs, isomorphic decision is needed since one graph data can contain lots of duplicated patterns. Therefore, we need to consider only patterns without duplications. However, these operations can cause enormous overheads due to knotty characteristics of graphs, which is called NP-hard problem. In addition, there also exists a problem that exponentially increases the number of unnecessary operations whenever any pattern size grows. In this paper, we propose a method that enhances efficiency of isomorphic decision in cyclic graphs based on a state-of-the-art algorithm, Gaston, which is called Egaston-CS (Efficient gaston for Cyclic-edge and Spanning-tree). In experiments, we compare our algorithm with previous algorithms, and thereby we demonstrate that Egaston-CS outperforms the others in terms of isomorphic decision." @default.
- W2271661574 created "2016-06-24" @default.
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- W2271661574 date "2012-12-11" @default.
- W2271661574 modified "2023-09-27" @default.
- W2271661574 title "Efficient Isomorphic Decision for Mining Sub Graphs with a Cyclic Form" @default.
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- W2271661574 doi "https://doi.org/10.1007/978-94-007-5860-5_114" @default.
- W2271661574 hasPublicationYear "2012" @default.
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