Discovering interesting patterns in large graph cubes

Demesmaeker, Florian;Ghrab, Amine;Nijssen, Siegfried;Skhiri, Sabri
(2017) Fourth International Workshop on High Performance Big Graph Data Management, Analysis, and Mining, in conjunction with the 2017 IEEE International Conference on Big Data (IEEE BigData 2017) — Location: Boston, MA, USA (11.December.2017)

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Authors
  • Demesmaeker, FlorianUCLouvain
    Author
  • Ghrab, AmineEuraNova
    Author
  • Nijssen, Siegfriedorcid-logoUCLouvain
    Author
  • Skhiri, SabriEuraNova
    Author
Abstract
Due to the increasing importance and volume of highly interconnected data, such as in social or information networks, a plethora of graph mining techniques have been designed to enable the analysis of such data. In this work, we focus on the mining of associations between entity features in networks. We model each entity feature as a dimension to be analyzed. Consequently we build our approach on top of the existing graph cube framework which is an extension of the concept of the data cube to networks. Our task is particularly challenging because it requires the analysis of both the initial multidimensional network and all its subsequent aggregate forms. As soon as we deal with a big data situation it is impossible for an analyst to consider manually all the possible views of the network data. The aim of this work is to design an algorithm for the discovery of interesting patterns in large graph cubes. Thus, instead of examining all the possible aggregations manually, the proposed technique leads the analyst to the interesting associations or patterns in the multidimensional network. Furthermore, we study the application of existing algorithms from the frequent itemset mining literature on graph data and propose a mapping between the two settings.
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Demesmaeker, F., Ghrab, A., Nijssen, S., & Skhiri, S. (2017). Discovering interesting patterns in large graph cubes. Proceedings of the Fourth International Workshop on High Performance Big Graph Data Management, Analysis, and Mining, in conjunction with the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), p. 3322-3331. https://doi.org/10.1109/BigData.2017.8258317