Mining Association Rules in Graphs Based on Frequent Cohesive Itemsets

  • Tayena Hendrickx
  • Boris Cule
  • Pieter Meysman
  • Stefan Naulaerts
  • Kris Laukens
  • Bart Goethals
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9078)


Searching for patterns in graphs is an active field of data mining. In this context, most work has gone into discovering subgraph patterns, where the task is to find strictly defined frequently re-occurring structures, i.e., node labels always interconnected in the same way. Recently, efforts have been made to relax these strict demands, and to simply look for node labels that frequently occur near each other. In this setting, we propose to mine association rules between such node labels, thus discovering additional information about correlations and interactions between node labels. We present an algorithm that discovers rules that allow us to claim that if a set of labels is encountered in a graph, there is a high probability that some other set of labels can be found nearby. Experiments confirm that our algorithm efficiently finds valuable rules that existing methods fail to discover.


Association Rule Mining Association Rule Node Label Nucleic Acid Research Frequent Subgraph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tayena Hendrickx
    • 1
  • Boris Cule
    • 1
  • Pieter Meysman
    • 1
  • Stefan Naulaerts
    • 1
  • Kris Laukens
    • 1
  • Bart Goethals
    • 1
  1. 1.University of AntwerpAntwerpBelgium

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