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gSpan: graph-based substructure pattern mining

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2 Author(s)
Yan, X. ; Dept. of Comput. Sci., Illinois Univ., Urbana, IL, USA ; Jiawei Han

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.

Published in:

Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on

Date of Conference:

2002