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 work we introduce lacon- and shrub-decompositions and use them to characterize transductions of bounded expansion graph classes and other graph classes. If one can efficiently compute sparse shrub- or lacon-decompositions of transductions of bounded expansion classes then one can solve every problem definable in first-order logic in linear time on these 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, 在被封闭式扩展图类中, 第一条线性逻辑定义的每一个问题都可以在线性时间内解决。 对稀有图类的第一顺序解释和转换导致更一般、密集的图形类, 似乎继承了它们稀有的对应方的许多良好的算法特性。 在这项工作中, 我们引入了 lacon- 和 shrub- decompetation, 并用它们来描述被封闭式扩展图类和其他图形类的转换。 如果能够有效地计算出被封闭式扩展类中稀有的灌木或拉孔分解的转换, 那么人们就可以在这些类别中直线时间解决所有在一阶性逻辑中可定义的问题。