Frequent subgraph mining based on the automorphism mapping

Frequent subgraph mining is an important research subject of graph mining. At present, there are many effective frequent subgraph mining algorithms, such as gSpan and FFSM. But these algorithms spend a lot of time solving the subgraph isomorphism or graph isomorphism problem, which affects the effic...

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Bibliographic Details
Published in2012 2nd International Conference on Computer Science and Network Technology pp. 1518 - 1522
Main Authors Gao, Zhengkang, Shang, Li, Jian, Yujiao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2012
Subjects
automorphism mapping
Costs
Dictionaries
extension location
frequent subgraph
graph mining
Indexing
Information science
labeled graph
Labeling
Semantics
Testing
Time complexity
Video signal processing
Web sites
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ISBN1467329630
9781467329637
DOI10.1109/ICCSNT.2012.6526208

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Summary:Frequent subgraph mining is an important research subject of graph mining. At present, there are many effective frequent subgraph mining algorithms, such as gSpan and FFSM. But these algorithms spend a lot of time solving the subgraph isomorphism or graph isomorphism problem, which affects the efficiency of the algorithm itself. According to the problem, we propose a novel frequent subgraph mining algorithm: FSMA, based on the automorphism mapping. The algorithm generate candidate subgraph through extending edges, and the extension location is determined by the automorphism mapping of subgraph. FSMA does not need to test the subgraph isomorphism or graph isomorphism throughout the process of mining frequent subgraph, so it achieves the time complexity of 0(n-2")(n is the number of frequent edges in graph dataset).
ISBN:1467329630
9781467329637
DOI:10.1109/ICCSNT.2012.6526208