Graphs

This list of references is preliminary and subject to future extensions.

Surveys

• State of the Art of Graph-based Data Mining
  T. Washio and H. Motoda
  ACM SIGKDD Explorations Newsletter 5(1):59-68
  ACM Press, New York, NY, USA 2003

Specific Articles

• Mining Molecular Fragments: Finding Relevant Substructures of Molecules
  C. Borgelt and M.R. Berthold
  Proc. 2nd IEEE Int. Conf. on Data Mining (ICDM 2002, Maebashi, Japan), 51-58
  IEEE Press, Piscataway, NJ, USA 2002
• On Canonical Forms for Frequent Graph Mining
  C. Borgelt
  Proc. 3rd Int. Workshop on Mining Graphs, Trees and Sequences (MGTS'05, Porto, Portugal), 1-12
  ECML/PKDD 2005 Organization Committee, Porto, Portugal 2005
• Graph-Based Data Mining
  D.J. Cook and L.B. Holder
  IEEE Trans. on Intelligent Systems 15(2):32-41
  IEEE Press, Piscataway, NJ, USA 2000
• Pharmacore Discovery Using the Inductive Logic Programming System PROGOL
  P.W. Finn, S. Muggleton, D. Page, and A. Srinivasan
  Machine Learning 30(2-3):241-270
  Kluwer, Amsterdam, Netherlands 1998
• Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
  J. Huan, W. Wang, and J. Prins
  Proc. 3rd IEEE Int. Conf. on Data Mining (ICDM 2003, Melbourne, FL), 549-552
  IEEE Press, Piscataway, NJ, USA 2003
• Molecular Feature Mining in HIV Data
  S. Kramer, L. de Raedt, and C. Helma
  Proc. 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2001, San Francisco, CA), 136-143
  ACM Press, New York, NY, USA 2001
• An Apriori-based Algorithm for Mining Frequent Substructures from Graph Data
  A. Inokuchi, T.&nsp;Washio, and H. Motoda
  PKDD 2000, 13-23
  Springer-Verlag, Berlin, Germany 2001
• Frequent Subgraph Discovery
  M. Kuramochi and G. Karypis
  Proc. 1st IEEE Int. Conf. on Data Mining (ICDM 2001, San Jose, CA), 313-320
  IEEE Press, Piscataway, NJ, USA 2001
• Finding Frequent Patterns in a Large Sparse Graph
  M. Kuramochi and G. Karypis
  Proc. 4th SIAM Int. Conf. on Data Mining (SDM 2004, Lake Buena Vista, FL)
  Society for Industrial and Applied Mathematics, Philadelphia, PA, USA 2004
• A Quickstart in Frequent Structure Mining Can Make a Difference
  S. Nijssen and J.N. Kok
  Proc. 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD2004, Seattle, WA), 647-652
  ACM Press, New York, NY, USA 2004
• Computing Frequent Graph Patterns from Semistructured Data
  N. Vanetik, E. Gudes, and S.E. Shimony
  Proc. IEEE Int. Conf. on Data Mining (ICDM 2002, Maebashi, Japan), 458-465
  IEEE Press, Piscataway, NJ, USA 2002
• gSpan: Graph-based Substructure Pattern Mining
  X. Yan and J. Han
  Proc. 2nd IEEE Int. Conf. on Data Mining (ICDM 2002, Maebashi, Japan), 721-724
  IEEE Press, Piscataway, NJ, USA 2002
• gSpan: Graph-based Substructure Pattern Mining
  X. Yan and J. Han
  Technical Report UIUCDCS-R-2002-2296, Department of Computer Science, University of Illinois at Urbana-Champaign, USA 2002
• Closegraph: Mining Closed Frequent Graph Patterns
  X. Yan and J. Han
  Proc. 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2003, Washington, DC), 286-295
  ACM Press, New York, NY, USA 2003