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gSpan: graph-based substructure pattern mining
Abstract
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.
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Cited by
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Top 10 algorithms in data mining
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Metadata
- Authors
- Yan, X., & Han, J.
- Venue
- 2002 IEEE International Conference on Data Mining, 2002. Proceedings.
- Publisher
- IEEE Comput. Soc
- Published
- 2003-06-26
- Pages
- 721-724
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2170726034
- Cited by
- 2,013
- References
- 0
- FWCI
- 64.19
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Yan, X., & Han, J. (2003). gSpan: graph-based substructure pattern mining. 2002 IEEE International Conference on Data Mining, 2002. Proceedings. https://doi.org/10.1109/icdm.2002.1184038
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