A (fully) dynamic graph algorithm is a data structure that supports edge insertions, edge deletions, and answers certain queries that are specific to the problem under consideration. There has been a lot of research on dynamic algorithms for graph problems that are solvable in polynomial time by a static algorithm. However, while there is a large body of theoretical work on efficient dynamic graph algorithms, a lot of these algorithms were never implemented and empirically evaluated. In this work, we consider the fully dynamic edge orientation problem, also called fully dynamic $\Delta$-orientation problem, which is to maintain an orientation of the edges of an undirected graph such that the out-degree is low. If edges are inserted or deleted, one may have to flip the orientation of some edges in order to avoid vertices having a large out-degree. While there has been theoretical work on dynamic versions of this problem, currently there is no experimental evaluation available. In this work, we close this gap and engineer a range of new dynamic edge orientation algorithms as well as algorithms from the current literature. Moreover, we evaluate these algorithms on real-world dynamic graphs. The best algorithm considered in this paper in terms of quality, based on a simple breadth-first search, computes the optimum result more than 90% of the instances and is on average only 2.4% worse than the optimum solution.
翻译:动态图表算法是一个支持边缘插入、 边缘删除和回答特定问题特定问题的数据结构。 已经对通过静态算法在多元时间可以溶解的图形问题的动态算法进行了大量研究。 但是, 虽然在高效动态图形算法方面有大量理论工作, 许多这些算法从未实施, 也没有经验评估。 在这项工作中, 我们考虑的是完全动态边缘方向问题, 也称为完全动态的 $\ Delta$- 方向问题, 即保持非方向图表边缘的方向, 使外度低。 如果边缘被插入或删除, 可能需要翻转一些边缘的定位, 以避免有较大度的逆向。 虽然在动态图表方面有很多理论工作, 目前还没有实验性评估。 在这项工作中, 我们缩小了这一差距, 并设计了一系列新的动态边缘方向算法以及当前文献的算法。 此外, 在现实- 质量 度 的深度分析中, 最糟糕的是, 我们评估这些最简单的算法, 也就是在现实- 水平 水平 图表中, 最高级的搜索结果 。