Distributed Enumeration of Four Node Graphlets at Quadrillion-Scale

Xiaozhou Liu, Yudi Santoso, Venkatesh Srinivasan, Alex Thomo
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引用次数: 1

Abstract

Graphlet enumeration is a basic task in graph analysis with many applications. Thus it is important to be able to perform this task within a reasonable amount of time. However, this objective is challenging when the input graph is very large, with millions of nodes and edges. Known solutions are limited in terms of scalability. Distributed computing is often proposed as a solution to improve scalability. However, it has to be done carefully to reduce the overhead cost and to really benefit from the distributed solution. We study the enumeration of four-node graphlets in undirected graphs using a distributed platform. We propose an efficient distributed solution which significantly surpasses the existing solutions. With this method we are able to process larger graphs that have never been processed before and enumerate quadrillions of graphlets using a modest cluster of machines. We show the scalability of our solution through experimental results. Finally, we also extend our algorithm to enumerate graphlets in probabilistic graphs and demonstrate its suitability for this case.
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千万亿规模下四节点石墨烯的分布式枚举
在许多应用程序中,Graphlet枚举是图分析中的一项基本任务。因此,能够在合理的时间内执行此任务非常重要。然而,当输入图非常大,有数百万个节点和边时,这个目标是具有挑战性的。已知的解决方案在可伸缩性方面是有限的。分布式计算通常被认为是提高可伸缩性的一种解决方案。然而,为了减少间接成本并真正从分布式解决方案中获益,必须谨慎地进行此操作。利用分布式平台研究了无向图中四节点石墨烯的枚举问题。我们提出了一种高效的分布式解决方案,大大超越了现有的解决方案。通过这种方法,我们能够处理以前从未处理过的更大的图,并使用适度的机器集群枚举千万亿的图。通过实验结果证明了该解决方案的可扩展性。最后,我们还扩展了我们的算法来枚举概率图中的graphlet,并证明了它对这种情况的适用性。
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