Querying Large Graph Databases

  • Yiping Ke
  • James Cheng
  • Jeffrey Xu Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)


Graph exists ubiquitously in a wide spectrum of application domains, such as protein structures in biology, chemical compounds in chemistry, food webs in ecology, social networks, Web graphs, P2P networks, and many more. With the increasing popularity of graph databases, how to assess graph data effectively and efficiently becomes an important research problem. Considerable research efforts have been devoted to developing advanced query processing techniques on graph databases. This tutorial presents a comprehensive survey on methodologies and techniques for querying large graph databases, including subgraph and supergraph query processing, structural similarity query processing, correlation search in transaction graph databases, connection query processing and approximate matching in large graphs. The tutorial is prepared for database and data mining researchers who are interested in complex data types that can be generally modeled as graphs.


Query Processing Large Graph Graph Database Graph Query Approximate Match 
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 2010

Authors and Affiliations

  • Yiping Ke
    • 1
  • James Cheng
    • 2
  • Jeffrey Xu Yu
    • 1
  1. 1.Department of Systems Engineering and Engineering ManagementThe Chinese University of Hong KongShatin, New TerritoriesHong Kong
  2. 2.Division of Information Systems School of Computer EngineeringNanyang Technological UniversitySingapore

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