SCARAB: scaling reachability computation on large graphs

R. Jin, Ning Ruan, S. Dey, J. Yu
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引用次数: 88

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.
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SCARAB:在大图形上的缩放可达性计算
大多数现有的可达性索引在中小型图上表现良好,但在100万个顶点/边左右会遇到可扩展性瓶颈。随着图变得越来越大,可伸缩性迅速成为当今可达性计算的主要研究挑战。我们是否可以构建索引,以缩放到具有数千万个顶点和边的图?现有的在中等规模图上表现良好的可达性指数是否可以扩展到非常大的图上?本文提出了一种统一的可达性计算框架SCARAB (SCAlable ReachABility,可扩展可达性),它不仅可以扩展现有的最先进的可达性指标,这些指标只能在中等大小的图上构建和工作,而且可以帮助加快在线查询回答方法的速度。我们的实验结果表明,SCARAB可以在具有数百万个顶点/边的图上执行,并且比最先进的可扩展性索引方法GRAIL快得多。
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