Multivariate Algorithmics for Temporal Graph Problems (MATE)

在第一个项目阶段，我们的目标是开发有效的（参数化）算法的时间图相关的中心问题。与第一个项目阶段相比，我们计划研究的主题发生了变化。我们要调查的主题是-新的时间概念，如时间图类，参数和问题类;-问题相关的旅程和时间的连接;-问题相关的时间子图detecting.We要扩展目前的文献主要集中在准确的组合算法的背景下，多元复杂性分析。也就是说，我们希望识别相关的参数化，并研究它们对问题复杂性的影响。为此，我们需要扩展参数化算法的工具箱，采用新的方法处理给定问题的时间方面。

As in the first project phase, we aim to develop efficient (parameterized) algorithms for central problems related to temporal graphs. Wehave a shift in the topics we plan to study when compared to the first project phase. The topics we want to investigate are- new temporal concepts such as temporal graph classes, parameters, and problem classes;- problems related to journeys and temporal connectivity;- problems related to temporal subgraph detection.We want to extend the current literature by concentrating mostly on exact combinatorial algorithms in the context of multivariate complexity analysis. That is, we want to identify relevant parameterizations and study their influence on the complexity of our problems. To this end, we need to extend the toolbox of parameterized algorithmics with new approaches that deal with the temporal aspects of the given problems.