Probabilistic Graph Summarization

  • Nasrin Hassanlou
  • Maryam Shoaran
  • Alex Thomo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7923)


We study group-summarization of probabilistic graphs that naturally arise in social networks, semistructured data, and other applications. Our proposed framework groups the nodes and edges of the graph based on a user selected set of node attributes. We present methods to compute useful graph aggregates without the need to create all of the possible graph-instances of the original probabilistic graph. Also, we present an algorithm for graph summarization based on pure relational (SQL) technology. We analyze our algorithm and practically evaluate its scalability using an extended Epinions dataset as well as synthetic datasets. The experimental results show that our algorithm produces compressed summary graphs in reasonable time.


Regular Graph Synthetic Dataset Node Attribute Summary Graph Existence Probability 
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-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nasrin Hassanlou
    • 1
  • Maryam Shoaran
    • 1
  • Alex Thomo
    • 1
  1. 1.University of VictoriaVictoriaCanada

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