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An Efficient Index for Top-k Keyword Search on Social Networks

  • Xudong Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7418)

Abstract

Social networks (e.g., Facebook, Twitter) have attracted significant attention recently. Many users have a search requirement to find new or existing friendships with similar interests in social networks. A well-known computing model is keyword search, which provides a user-friendly interface to meet users search demands. However traditional keyword search techniques only consider the textual proximity and ignore the relationship closeness between different users. It is a big challenge to integrate social relationship and textual proximity and it calls for an effective method to support keyword search in social networks. To address these challenges, we present a tree decomposition based hierarchical keyword index structure (TDK-Index) to solve the problem. Our major contributions are: (1)TDK-Index which integrate keyword index and relationship closeness index as a whole; (2)Two-phase TA algorithm which narrows the threshold obviously compared to existing methods and speed up top-k query by a factor of two; and (3)flexible solution which adopts different application circumstances by parameter adjustment. Our experiments provide evidences of the efficiency and scalability of our solution.

Keywords

Random Access Ranking Function Keyword Search Tree Decomposition Local Index 
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 2012

Authors and Affiliations

  • Xudong Du
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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