A quantitative comparison of the subgraph miners MoFa, gSpan, FFSM, and Gaston. Wörlein, M., Meinl, T., Fischer, I., & Philippsen, M. In Proc. of Principles and Practice of Knowledge Discovery in Databases (PKDD 2005), volume 3721, of Lect Notes Comput Sci, pages 392–403, 2005.
doi  abstract   bibtex   
Several new miners for frequent subgraphs have been published recently. Whereas new approaches are presented in detail, the quantitative evaluations are often of limited value: only the performance on a small set of graph databases is discussed and the new algorithm is often only compared to a single competitor based on an executable. It remains unclear, how the algorithms work on bigger/other graph databases and which of their distinctive features is best suited for which database. We have re-implemented the subgraph miners MoFa, gSpan, FFSM, and Gaston within a common code base and with the same level of programming expertise and optimization effort. This paper presents the results of a comparative benchmarking that ran the algorithms on a comprehensive set of graph databases.
@InProceedings{woerlein05quantitative,
  author    = {W\"orlein, Marc and Meinl, Thorsten and Fischer, Ingrid and Philippsen, Michael},
  title     = {A quantitative comparison of the subgraph miners {MoFa}, {gSpan}, {FFSM}, and {Gaston}},
  booktitle = {Proc. of Principles and Practice of Knowledge Discovery in Databases (PKDD 2005)},
  year      = {2005},
  volume    = {3721},
  series    = lncs,
  pages     = {392--403},
  publisher = Springer,
  abstract  = {Several new miners for frequent subgraphs have been published recently. Whereas new approaches are presented in detail, the quantitative evaluations are often of limited value: only the performance on a small set of graph databases is discussed and the new algorithm is often only compared to a single competitor based on an executable. It remains unclear, how the algorithms work on bigger/other graph databases and which of their distinctive features is best suited for which database. We have re-implemented the subgraph miners MoFa, gSpan, FFSM, and Gaston within a common code base and with the same level of programming expertise and optimization effort. This paper presents the results of a comparative benchmarking that ran the algorithms on a comprehensive set of graph databases.},
  doi       = {10.1007/11564126_39},
  isbn      = {3-540-29244-6, 978-3-540-29244-9},
  keywords  = {chemoinformatics; subgraph isomorphism; data mining; subgraph mining},
  location  = {Porto, Portugal},
  owner     = {Sebastian},
  timestamp = {2012.06.07},
}

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