Hyperbolicity is a graph parameter related to how much a graph resembles a tree with respect to distances. Its computation is challenging as the main approaches consist in scanning all quadruples of the graph or using fast matrix multiplication as building block, both are not practical for large graphs. In this paper, we propose and evaluate an approach that uses a hierarchy of distance-k dominating sets to reduce the search space. This technique, compared to the previous best practical algorithms, enables us to compute the hyperbolicity of graphs with unprecedented size (up to a million nodes) and speeds up the computation of previously attainable graphs by up to 3 orders of magnitude while reducing the memory consumption by up to more than a factor of 23.
翻译:超偏度是一个图形参数, 与图表在距离方面与树的相似程度有关。 它的计算具有挑战性, 因为主要方法包括扫描图形的所有四重或将快速矩阵乘法作为积块, 两者对大图都不切实际。 在本文中, 我们提议并评价一种方法, 使用远程- k 主导数据集的等级来缩小搜索空间。 与以往的最佳实用算法相比, 这一技术使我们能够计算出具有前所未见尺寸( 最高为百万节点)的图形的双向性, 并加快计算出前可实现的图形, 最多3个数量级, 同时将存储耗减到23个以上。