Comparing the speed and accuracy of approaches to betweenness centrality approximation

Abstract

Background: 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. Methods: 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. 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. Conclusions: 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.