Towards Neighborhood Window Analytics over Large-Scale Graphs

  • Qi FanEmail author
  • Zhengkui Wang
  • Chee-Yong Chan
  • Kian-Lee Tan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9643)


Information networks are often modeled as graphs, where the vertices are associated with attributes. In this paper, we study neighborhood window analytics, namely k-hop window query, that aims to capture the properties of a local community involving the k-hop neighbors (defined on the graph structures) of each vertex. We develop a novel index, Dense Block Index (DBIndex), to facilitate efficient processing of k-hop window queries. Extensive experimental studies conducted over both real and synthetic datasets with hundreds of millions of vertices and edges show that our proposed solutions are four orders of magnitude faster in query performance than the non-index algorithm, and are superior over the state-of-the-art solution in terms of both scalability and efficiency.


Graph analytics Graph window Neighborhood aggregation 



Qi Fan is supported by NGS Scholarship. This work is supported by the MOE/NUS grant R-252-000-500-112 and AWS in Education Grant award.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Qi Fan
    • 1
    Email author
  • Zhengkui Wang
    • 2
  • Chee-Yong Chan
    • 3
  • Kian-Lee Tan
    • 1
    • 3
  1. 1.NUS Graduate School for Integrative Science and EngineeringSingaporeSingapore
  2. 2.Singapore Institute of TechnologySingaporeSingapore
  3. 3.School of ComputingNational University of SingaporeSingaporeSingapore

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