DB2SNA: An All-in-One Tool for Extraction and Aggregation of Underlying Social Networks from Relational Databases

  • Rania SoussiEmail author
  • Etienne Cuvelier
  • Marie-Aude Aufaure
  • Amine Louati
  • Yves Lechevallier
Part of the Lecture Notes in Social Networks book series (LNSN, volume 6)


In the enterprise context, People need to visualize different types of interactions between heterogeneous objects (e.g. product and site, customers and product, people interaction (social network)). The existing approaches focus on social networks extraction using web document. However a considerable amount of information is stored in relational databases. Therefore, relational databases can be seen as rich sources for extracting a social network. The extracted network has in general a huge size which makes it difficult to analyze and visualize. An aggregation step is needed in order to have more understandable graphs. In this chapter, we propose a heterogeneous object graph extraction approach from a relational database and we present its application to extract social network. This step is followed by an aggregation step in order to improve the visualisation and the analyse of the extracted social network. Then, we aggregate the resulting network using the k-SNAP algorithm which produces a summarized graph.


Social Network Relational Database Community Detection Graph Transformation Graph Database 
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.



This work is partially financed by the ARSA project (Social Networks Analysis for Public Administrations) and by the STIC INRIA-Tunisia project “Social network exploration for recommender systems”. The Academic chair in Business Intelligence is funded by SAP.


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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Rania Soussi
    • 1
    Email author
  • Etienne Cuvelier
    • 1
  • Marie-Aude Aufaure
    • 1
    • 2
  • Amine Louati
    • 2
    • 3
  • Yves Lechevallier
    • 2
  1. 1.Ecole Centrale Paris, MAS Laboratory, Business Intelligence TeamChatenay-MalabryFrance
  2. 2.INRIA Paris-RocquencourtAxis TeamRocquencourtFrance
  3. 3.ENSI, RIADI-GDL LaboratoryCampus Universitaire de la ManoubaManoubaTunisia

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