We study how we can accelerate the spreading of information in temporal graphs via delaying operations; a problem that captures real-world applications varying from information flows to distribution schedules. In a temporal graph there is a set of fixed vertices and the available connections between them change over time in a predefined manner. We observe that, in some cases, the delay of some connections can in fact decrease the time required to reach from some vertex (source) to another vertex (target). We study how we can minimize the maximum time a set of source vertices needs to reach every other vertex of the graph when we are allowed to delay some of the connections of the graph. For one source, we prove that the problem is W[2]-hard and NP-hard, when parameterized by the number of allowed delays. On the other hand, we derive a polynomial-time algorithm for one source and unbounded number of delays. This is the best we can hope for; we show that the problem becomes NP-hard when there are two sources and the number of delays is not bounded. We complement our negative result by providing an FPT algorithm parameterized by the treewidth of the graph plus the lifetime of the optimal solution. Finally, we provide polynomial-time algorithms for several classes of graphs.
翻译:我们研究如何通过延迟操作加速在时间图中传播信息;这是一个从信息流到分布时间表的不同应用捕捉真实世界应用程序的问题。在时间图中,有一套固定的脊椎和它们之间的现有联系会以预先确定的方式随时间而变化。我们观察到,在某些情况下,某些连接的延迟实际上会减少从某个顶点(源)到另一个顶点(目标)所需的时间。我们研究如何最大限度地减少一个源头需要达到的最大时间;当允许我们延迟图形的某些连接时,一套源头需要达到该图的每一个其它顶点。对于一个来源,我们证明问题是W[2]硬的和NP-硬的,按允许延迟的次数参数来比较。另一方面,我们从一个源头(源)到另一个顶点(源)到另一个顶点(目标)的多重时间算法可以减少所需的时间。这是我们所希望的最好办法;我们发现,当有两个来源和延迟次数没有被限制时,问题就会变得很硬。我们用一个最坏的结果来补充我们最坏的结果,我们用一个最坏的FPT的图表级算法,我们最后提供了一个最坏的模型的模型的模型。