Improving Functional Modularity in Protein-Protein Interactions Graphs Using Hub-Induced Subgraphs

  • Duygu Ucar
  • Sitaram Asur
  • Umit Catalyurek
  • Srinivasan Parthasarathy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


Dense subgraphs of Protein-Protein Interaction (PPI) graphs are believed to be potential functional modules and play an important role in inferring the functional behavior of proteins. PPI graphs are known to exhibit the scale-free property in which a few nodes (hubs) are highly connected. This scale-free topology of PPI graphs makes it hard to isolate dense subgraphs effectively. In this paper, we propose a novel refinement method based on neighborhoods and the biological importance of hub proteins. We show that this refinement improves the functional modularity of the PPI graph and leads to effective clustering into dense components. A detailed comparison of these dense components with the ones obtained from the original PPI graph reveal three major benefits of the refinement: i) Enhancement of existing functional groupings; ii) Isolation of new functional groupings; and iii) Soft clustering of multifunctional hub proteins to multiple functional groupings.


Functional Module Original Graph Nuclear Pore Complex Dense Component Cluster Score 
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 2006

Authors and Affiliations

  • Duygu Ucar
    • 1
  • Sitaram Asur
    • 1
  • Umit Catalyurek
    • 2
  • Srinivasan Parthasarathy
    • 1
    • 2
  1. 1.Department of Computer Science and EngineeringThe Ohio State University 
  2. 2.Department of Biomedical InformaticsThe Ohio State University 

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