The concept of bounded expansion provides a robust way to capture sparse graph classes with interesting algorithmic properties. Most notably, every problem definable in first-order logic can be solved in linear time on bounded expansion graph classes. First-order interpretations and transductions of sparse graph classes lead to more general, dense graph classes that seem to inherit many of the nice algorithmic properties of their sparse counterparts. In this paper, we show that one can encode graphs from a class with structurally bounded expansion via lacon-, shrub- and parity-decompositions from a class with bounded expansion. These decompositions are useful for lifting properties from sparse to structurally sparse graph classes.
翻译:封闭式扩展概念为捕捉具有有趣的算法特性的稀有图表类提供了一种强有力的方法。 最显著的是, 最明显的是, 在被封闭式扩展图类中, 最先确定的第一阶逻辑的每一个问题都可以在线性时间内解决。 对稀有式图表类的第一阶解释和转换导致更一般、密集的图表类, 似乎继承了它们稀有式对应方的许多良好的算法特性。 在本文中, 我们显示, 可以通过 lacon- 、 shrub- 和 等同式解析法将那些通过 lacon- 、 shrub- 和 等同式解析进行结构性扩展的图表类中的图表编码。 这些分解对于从稀有式到结构性稀有式图表类的特性是有用的。