Many applications need to execute Single-Source Shortest Paths (SSSP) algorithm on each snapshot of a time-evolving graph, leading to long waiting times experienced by the users of such applications. However, these applications are often time-sensitive, the delayed computation results can lead to the loss of best decision-making opportunities. To address this problem, in this paper we propose an efficient SSSP algorithm for time-evolving graphs, called V-Grouper. The main idea of V-Grouper is to avoid the redundant computations of the same vertex in different snapshots. Our experimental results over real-world time-evolving graphs show that, due to the high similarity of consecutive snapshots, the computation results of one vertex in neighboring snapshots are equal with a high probability. At the beginning of computation, V-Grouper first divides all the versions of a given vertex in different snapshots into vertex groups, where the computation result of each version is predicted based on the aforementioned insight of neighboring snapshots having equal results. The versions of the vertex in each group have the same predicted computation result. During the computation process for each vertex group, only one version needs to participate in computation, avoiding a large number of redundant computations. Experimental results show that V-Grouper is up to 64.31× faster than the state-of-the-art SSSP algorithm.

许多应用程序需要在时间演化图的每个快照上执行单源最短路径（SSSP）算法，导致此类应用程序的用户经历很长的等待时间。然而，这些应用程序往往对时间敏感，延迟的计算结果可能导致失去最佳决策机会。为了解决这个问题，在本文中，我们提出了一种针对时间演化图的高效 SSSP 算法，称为 V-Grouper。V-Grouper的主要思想是避免同一顶点在不同快照中的冗余计算。我们对现实世界时间演化图的实验结果表明，由于连续快照的高度相似性，相邻快照中一个顶点的计算结果很可能是相等的。在计算开始时，V-Grouper首先将不同快照中给定顶点的所有版本划分为顶点组，其中每个版本的计算结果是基于前面提到的具有相同结果的相邻快照的洞察来预测的。每组中顶点的版本具有相同的预测计算结果。每个顶点组的计算过程中，只需要一个版本参与计算，避免了大量的冗余计算。实验结果表明，V-Grouper 比最先进的 SSSP 算法快 64.31 倍。