SCARAB: scaling reachability computation on large graphs

  • Authors:

  • Ruoming Jin;Ning Ruan;Saikat Dey;Jeffrey Yu Xu

  • Affiliations:

  • Kent State University, Kent, OH, USA;Kent State University, Kent, OH, USA;Kent State University, Kent, OH, USA;Chinese University of Hong Kong, Hong Kong, China

  • Venue:
  • SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
  • Year:
  • 2012

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Abstract

Most of the existing reachability indices perform well on small- to medium- size graphs, but reach a scalability bottleneck around one million vertices/edges. As graphs become increasingly large, scalability is quickly becoming the major research challenge for the reachability computation today. Can we construct indices which scale to graphs with tens of millions of vertices and edges? Can the existing reachability indices which perform well on moderate-size graphs be scaled to very large graphs? In this paper, we propose SCARAB (standing for SCAlable ReachABility), a unified reachability computation framework: it not only can scale the existing state-of-the-art reachability indices, which otherwise could only be constructed and work on moderate size graphs, but also can help speed up the online query answering approaches. Our experimental results demonstrate that SCARAB can perform on graphs with millions of vertices/edges and is also much faster then GRAIL, the state-of-the-art scalability index approach.