gSpan: Graph-Based Substructure Pattern Mining

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gSpan: Graph-Based Substructure Pattern Mining
We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based Substructure pattern mining), which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its canonical label. Based on this lexicographic order, gSpan adopts the depth-first search strategy to mine frequent connected subgraphs efficiently. Our performance study shows that gSpan substantially outperforms previous algorithms, sometimes by an order of magnitude.
Xifeng Yan, Jiawei Han
Added 14 Jul 2010
Updated 14 Jul 2010
Type Conference
Year 2002
Where ICDM
Authors Xifeng Yan, Jiawei Han
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