Knowledge and Information Systems

, Volume 50, Issue 2, pp 569–584 | Cite as

Attributed graph mining in the presence of automorphism

  • Claude Pasquier
  • Frédéric Flouvat
  • Jérémy Sanhes
  • Nazha Selmaoui-Folcher
Regular Paper


Attributed directed graphs are directed graphs in which nodes are associated with sets of attributes. Many data from the real world can be naturally represented by this type of structure, but few algorithms are able to directly handle these complex graphs. Mining attributed graphs is a difficult task because it requires combining the exploration of the graph structure with the identification of frequent itemsets. In addition, due to the combinatorics on itemsets, subgraph isomorphisms (which have a significant impact on performances) are much more numerous than in labeled graphs. In this paper, we present a new data mining method that can extract frequent patterns from one or more directed attributed graphs. We show how to reduce the combinatorial explosion induced by subgraph isomorphisms thanks to an appropriate processing of automorphic patterns.


Attributed graph Frequent pattern mining Automorphism Structure mining Itemset mining 



This work was supported by the ANR Grant “FOSTER” ANR-2010-COSI-012-01.


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Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Claude Pasquier
    • 1
    • 2
    • 3
  • Frédéric Flouvat
    • 3
  • Jérémy Sanhes
    • 3
  • Nazha Selmaoui-Folcher
    • 3
  1. 1.Univ. Nice Sophia Antipolis, I3S, UMR 7271Sophia AntipolisFrance
  2. 2.CNRS, I3S, UMR 7271Sophia AntipolisFrance
  3. 3.Multidisciplinary Research Team on Material and Environment (PPME)University of New CaledoniaNouméaNew Caledonia

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