Jaccard Coefficients as a Potential Graph Benchmark
Jaccard Coefficients as a Potential Graph Benchmark
复制标题
DOI：
10.1109/ipdpsw.2016.208
复制
发表时间：
2016-05
期刊：
影响因子：
--
通讯作者：
P. Kogge
中科院分区：
文献类型：
--
作者：
P. Kogge
随着图的大小快速增长，图的处理在许多应用中变得越来越重要。与科学计算一样，越来越需要了解系统架构和图算法之间的关系，特别是随着系统规模和图大小的增加。迄今为止，有一个这样的图形基准测试，它有数百个可用的比较报告，即广度优先搜索，它在过去几年中推动了新算法的发展，显着提高了典型性能。本文提出了一个基于邻域和杰卡德系数计算的附加基准，该基准具有不同的内在复杂性，并且可以通过多种方式重新构建，以适合不同类别的实际应用。
The processing of graphs is of increasing importance in many applications, with the size of such graphs growing rapidly. As with scientific computing, there is a growing need to understand the relationship between system architectures and graph algorithms, especially as both the scale of the system and the size of the graph increase. To date there is one such graph benchmark that has several hundred comparative reports available, namely Breadth First Search, which has over the last few years fueled new algorithms that have improved typical performance very significantly. This paper suggests an additional benchmark based on the computation of neighborhoods and Jaccard coefficients that is of both a different intrinsic complexity and can be recast in multiple ways that may be suitable for different classes of real-world applications.