Many algorithms require doing a large number of betweenness centrality calculations quickly, and accommodating this need is an active open research area. There are many different ideas and approaches to speeding up these calculations, and it is difficult to know which approach will work best in practical situations. The current study attempts to judge performance of betweenness centrality approximation algorithms by running them under conditions that practitioners are likely to experience. For several approaches to approximation, we run two tests, clustering and immunization, on identical hardware, along with a process to determine appropriate parameters. This allows an across-the-board comparison of techniques based on the dimensions of speed and accuracy of results. Overall, the speed of betweenness centrality can be reduced several orders of magnitude by using approximation algorithms. We find that the speeds of individual algorithms can vary widely based on input parameters. The fastest algorithms utilize parallelism, either in the form of multi-core processing or GPUs. Interestingly, getting fast results does not require an expensive GPU. The methodology presented here can guide the selection of a betweenness centrality approximation algorithm depending on a practitioner’s needs and can also be used to compare new methods to existing ones.

中文翻译：

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比较中间性中心法的速度和准确性

许多算法需要快速进行大量中间度计算，而适应这种需求是活跃的开放研究领域。有许多不同的想法和方法可以加快这些计算的速度，很难知道哪种方法在实际情况下最有效。当前的研究试图通过在从业者可能会遇到的条件下运行中间性中心性近似算法来判断它们的性能。对于几种近似方法，我们在相同的硬件上运行两个测试（聚类和免疫），以及确定合适参数的过程。这样就可以根据速度和结果准确性对技术进行全面的比较。总体，通过使用近似算法，可以将中间性中心速度降低几个数量级。我们发现，基于输入参数，各个算法的速度可以有很大差异。最快的算法以多核处理或GPU的形式利用并行性。有趣的是，获得快速结果不需要昂贵的GPU。这里介绍的方法可以根据从业者的需求指导中间度中心度近似算法的选择，也可以用于将新方法与现有方法进行比较。获得快速结果不需要昂贵的GPU。这里介绍的方法可以根据从业者的需求指导中间度中心度近似算法的选择，也可以用于将新方法与现有方法进行比较。获得快速结果不需要昂贵的GPU。这里介绍的方法可以根据从业者的需求指导中间度中心度近似算法的选择，也可以用于将新方法与现有方法进行比较。