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One Edge at a Time: A Novel Approach Towards Efficient Transitive Reduction Computation on DAGs

机译:一次一个边缘:一种对DAG有效减少计算的新方法

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摘要

Given a directed acyclic graph (DAG) G, G's transitive reduction (TR) Gtr is the unique DAG satisfying that Gtr has the minimum number of edges and has the same transitive closure (TC) as G. TR computation has been extensively studied during the past decades and was used in many applications, where the main problem is how to compute TR efficiently for large graphs. However, existing approaches have either large space complexity or higher time complexity, which makes them cannot compute TR efficiently on large dense graphs. We propose a novel approach for TR computation, which takes every single edge as the basic processing unit, and utilizes existing reachability algorithms to test whether it is redundant or not. In this way, we avoid the costly graph traversal operation of existing approaches. We identify the performance bottleneck and propose a set of heuristics to sort edges, such that to reduce the average processing cost of each edge. We show by experimental results that our approach works much better than all the existing approaches, and can be faster than the state-of-the-art approach by more than two orders of magnitude on large dense graphs.
机译:给定针向非循环图(DAG)G,G的传递减少(TR)GTR是满足GTR具有最小数量的唯一DAG并且具有与G. TR计算的相同的传递闭合(TC)在广泛研究期间过去几十年并用于许多应用程序,主要问题是如何为大图有效地计算TR。然而,现有方法具有大的空间复杂性或更高的时间复杂性,这使得它们无法在大密集图上有效地计算TR。我们提出了一种新的TR计算方法,它将每一个边缘都作为基本处理单元,并利用现有的可达性算法来测试它是否是冗余的。通过这种方式,我们避免了昂贵的图形遍历现有方法的遍历。我们识别性能瓶颈并提出一组启发式来排序边缘,从而降低每个边缘的平均处理成本。我们通过实验结果表明,我们的方法优于所有现有方法,并且可以比最先进的方法更快,在大密集图上超过两个数量级。

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