Landmark-based 3-hop cover labeling is a category of approaches for shortest distance/path queries on large-scale complex networks. It pre-computes an index offline to accelerate the online distance/path query. Most real-world graphs undergo rapid changes in topology, which makes index maintenance on dynamic graphs necessary. So far, the majority of index maintenance methods can handle only one edge update (either an addition or deletion) each time. To keep up with frequently changing graphs, we research the fully batch maintenance problem for the 3-hop cover labeling, and proposed the method called FulBM. FulBM is composed of two algorithms: InsBM and DelBM, which are designed to handle batch edge insertions and deletions respectively. This separation is motivated by the insight that batch maintenance for edge insertions are much more time-efficient, and the fact that most edge updates in the real world are incremental. Both InsBM and DelBM are equipped with well-designed pruning strategies to minimize the number of vertex accesses. We have conducted comprehensive experiments on both synthetic and real-world graphs to verify the efficiency of FulBM and its variants for weighted graphs. The results show that our methods achieve 5.5 × to 228 × speedup compared with the state-of-the-art method.

基于地标的 3 跳覆盖标记是大规模复杂网络上最短距离/路径查询的一类方法。它离线预先计算索引，以加速在线距离/路径查询。大多数现实世界的图的拓扑结构都会发生快速变化，这使得动态图的索引维护变得必要。到目前为止，大多数索引维护方法每次只能处理一次边缘更新（添加或删除）。为了跟上频繁变化的图，我们研究了3 跳覆盖标记的完全批量 维护问题，并提出了称为FulBM的方法。FulBM由两种算法组成：InsBM和DelBM，分别设计用于处理批量边缘插入和删除。这种分离的动机是这样的认识：边缘插入的批量维护更加省时，而且现实世界中的大多数边缘更新都是增量的。InsBM 和 DelBM 都配备了精心设计的剪枝策略，以最大限度地减少顶点访问的数量。我们对合成图和真实图进行了全面的实验，以验证 FulBM 及其加权图变体的效率。结果表明，与最先进的方法相比，我们的方法实现了 5.5 倍到 228 倍的加速。