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Heuristics for Semi-External Depth First Search on Directed Graphs

机译:有向图上半外部深度优先搜索的启发式方法

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摘要

Computing the strong components of a directed graph is an essential operation for a basic structural analysis of it. This problem can be solved by twice running a depth-first search (DFS). In an external setting, in which all data can no longer be stored in the main memory, the DFS problem is unsolved so far. Assuming that node-related data can be stored internally, semi-external computing does not make the problem substantially easier. Considering the definite need to analyze very large graphs, we have developed a set of heuristics which together allow the performance of semi-external DFS for directed graphs in practice. The heuristics have been applied to graphs with very different graph properties, including "web graphs" as described in the most recent literature and some large call graphs from ATT. Depending on the graph structure, the program is between 10 and 200 times faster than the best alternative, a factor that will further increase with future technological developments.
机译:计算有向图的强大组成部分是对其进行基本结构分析的基本操作。可以通过两次运行深度优先搜索(DFS)来解决此问题。在外部设置中,所有数据都无法再存储在主存储器中,到目前为止,DFS问题尚未解决。假设可以在内部存储与节点有关的数据,那么半外部计算不会使问题变得更加容易。考虑到绝对需要分析非常大的图,我们开发了一套启发式方法,可以结合使用启发式方法在实践中为定向图执行半外部DFS。该启发式方法已应用于具有非常不同的图形属性的图形,包括最新文献中所述的“网络图形”和ATT的一些大型调用图形。根据图形结构的不同,该程序的速度比最佳方案快10到200倍,随着未来技术的发展,这一因素将进一步增加。

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