Efficient Isomorphic Decision for Mining Sub Graphs with a Cyclic Form
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.
KeywordsSub graph mining Cyclic graph Pattern expansion Graph isomorphic decision
This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF No. 2012-0003740 and 2012-0000478).
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