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.

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  1. Ferré, Sébastien; Cellier, Peggy: Graph-FCA: an extension of formal concept analysis to knowledge graphs (2020)
  2. Haraguchi, Kazuya; Momoi, Yusuke; Shurbevski, Aleksandar; Nagamochi, Hiroshi: COOMA: a components overlaid mining algorithm for enumerating connected subgraphs with common itemsets (2019)
  3. van der Hallen, Matthias; Paramonov, Sergey; Janssens, Gerda; Denecker, Marc: Knowledge representation analysis of graph mining (2019)
  4. Ravkic, Irma; Žnidaršič, Martin; Ramon, Jan; Davis, Jesse: Graph sampling with applications to estimating the number of pattern embeddings and the parameters of a statistical relational model (2018)
  5. Strüber, D.; Rubin, J.; Arendt, T.; Chechik, M.; Taentzer, G.; Plöger, J.: Variability-based model transformation: formal foundation and application (2018)
  6. Costa, Fabrizio: Learning an efficient constructive sampler for graphs (2017)
  7. Gkantouna, Vassiliki; Tzimas, Giannis: Mining domain-specific design patterns: a case study (2017)
  8. Hong, Jihye; Park, Kisung; Han, Yongkoo; Rasel, Mostofa Kamal; Vonvou, Dawanga; Lee, Young-Koo: Disk-based shortest path discovery using distance index over large dynamic graphs (2017)
  9. Ahmed, Akiz Uddin; Ahmed, Chowdhury Farhan; Samiullah, Md.; Adnan, Nahim; Leung, Carson Kai-Sang: Mining interesting patterns from uncertain databases (2016)
  10. Strüber, Daniel; Rubin, Julia; Arendt, Thorsten; Chechik, Marsha; Taentzer, Gabriele; Plöger, Jennifer: \textitRuleMerger: automatic construction of variability-based model transformation rules (2016)
  11. Talukder, N.; Zaki, M. J.: A distributed approach for graph mining in massive networks (2016)
  12. Uno, Takeaki; Uno, Yushi: Mining preserving structures in a graph sequence (2016)
  13. Dahm, Nicholas; Bunke, Horst; Caelli, Terry; Gao, Yongsheng: Efficient subgraph matching using topological node feature constraints (2015)
  14. Du, Lingxia; Li, Cuiping; Chen, Hong; Tan, Liwen; Zhang, Yinglong: Probabilistic SimRank computation over uncertain graphs (2015)
  15. Erciyes, K.: Distributed and sequential algorithms for bioinformatics (2015)
  16. Khan, Kifayat Ullah; Nawaz, Waqas; Lee, Young-Koo: Set-based approximate approach for lossless graph summarization (2015)
  17. Pan, Shirui; Wu, Jia; Zhu, Xingquan; Long, Guodong; Zhang, Chengqi: Finding the best not the most: regularized loss minimization subgraph selection for graph classification (2015)
  18. Shahrivari, Saeed; Jalili, Saeed: Distributed discovery of frequent subgraphs of a network using MapReduce (2015)
  19. Frasconi, Paolo; Costa, Fabrizio; De Raedt, Luc; De Grave, Kurt: kLog: a language for logical and relational learning with kernels (2014)
  20. Galbrun, Esther; Kimmig, Angelika: Finding relational redescriptions (2014)

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