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Main-memory triangle computations for very large (sparse (power-law)) graphs

机译:超大图(稀疏(幂律)图)的主内存三角形计算

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Finding, counting and/or listing triangles (three vertices with three edges) in massive graphs are natural fundamental problems, which have recently received much attention because of their importance in complex network analysis. Here we provide a detailed survey of proposed main-memory solutions to these problems, in a unified way. We note that previous authors have paid surprisingly little attention to space complexity of main-memory solutions, despite its both fundamental and practical interest We therefore detail space complexities of known algorithms and discuss their implications. We also present new algorithms which are time optimal for triangle listing and beats previous algorithms concerning space needs. They have the additional advantage of performing better on power-law graphs, which we also detail. We finally show with an experimental study that these two algorithms perform very well in practice, allowing us to handle cases which were previously out of reach.
机译:在大量图中查找,计数和/或列出三角形(三个边缘的三个顶点)是自然的基本问题,由于它们在复杂的网络分析中的重要性,最近备受关注。在这里,我们以统一的方式详细介绍了针对这些问题的建议的主内存解决方案。我们注意到,尽管前者对基本存储器解决方案的空间复杂性有基本和实际的兴趣,但先前的作者却很少对此给予关注,因此我们详细介绍了已知算法的空间复杂性并讨论了其含义。我们还提出了新的算法,这些算法对于三角形列表而言是时间最佳的,并且优于有关空间需求的先前算法。它们还具有在幂律图上表现更好的额外优势,我们还将对此进行详细介绍。最后,我们通过实验研究表明,这两种算法在实践中均表现出色,从而使我们能够处理以前无法达到的情况。

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