Optimized Graph Search Using Multi-Level Graph Clustering

  • Rahul Kala
  • Anupam Shukla
  • Ritu Tiwari
Part of the Communications in Computer and Information Science book series (CCIS, volume 40)


Graphs find a variety of use in numerous domains especially because of their capability to model common problems. The social networking graphs that are used for social networking analysis, a feature given by various social networking sites are an example of this. Graphs can also be visualized in the search engines to carry search operations and provide results. Various searching algorithms have been developed for searching in graphs. In this paper we propose that the entire network graph be clustered. The larger graphs are clustered to make smaller graphs. These smaller graphs can again be clustered to further reduce the size of graph. The search is performed on the smallest graph to identify the general path, which may be further build up to actual nodes by working on the individual clusters involved. Since many searches are carried out on the same graph, clustering may be done once and the data may be used for multiple searches over the time. If the graph changes considerably, only then we may re-cluster the graph.


Clustering Searching Graph theory Social Networking Analysis Web Search Results 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rahul Kala
    • 1
  • Anupam Shukla
    • 1
  • Ritu Tiwari
    • 1
  1. 1.Department of Information TechnologyIndian Institute of Information Technology and Management GwaliorGwaliorIndia

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