Temporal graphs are graphs where the topology and/or other properties of the graph change with time. They have been used to model applications with temporal information in various domains. Problems on static graphs become more challenging to solve in temporal graphs because of dynamically changing topology, and many recent works have explored graph problems on temporal graphs. In this paper, we define a type of matching called {\em 0-1 timed matching} for temporal graphs, and investigate the problem of finding a {\em maximum 0-1 timed matching} for different classes of temporal graphs. We first prove that the problem is NP-Complete for rooted temporal trees when each edge is associated with two or more time intervals. We then propose an $O(n \log n)$ time algorithm for the problem on a rooted temporal tree with $n$ nodes when each edge is associated with exactly one time interval. The problem is then shown to be NP-Complete also for bipartite temporal graphs even when each edge is associated with a single time interval and degree of each node is bounded by a constant $k \geq 3$. We next investigate approximation algorithms for the problem for temporal graphs where each edge is associated with more than one time intervals. It is first shown that there is no $\frac{1}{n^{1-\epsilon}}$-factor approximation algorithm for the problem for any $\epsilon > 0$ even on a rooted temporal tree with $n$ nodes unless NP = ZPP. We then present a $\frac{5}{2\mathcal{N}^* + 3}$-factor approximation algorithm for the problem for general temporal graphs where $\mathcal{N^*}$ is the average number of edges overlapping in time with each edge in the temporal graph. The same algorithm is also a constant-factor approximation algorithm for degree bounded temporal graphs.
翻译:时间图形 { 时间图形 是图表的表层和/ 或其他属性 { 时间变化 { 时间变数 { 时间变数 } 的图形 。 我们首先证明,当每个边缘与两个或两个以上时间间隔相关时, 以时间图形为应用模型。 静态图形的问题变得更加难以在时间图形中解决。 许多最近的工作探索了时间图形中的图形问题 。 在本文中, 我们定义了一种名为 $ em 0-1 时间匹配} 的匹配类型, 并调查了为不同类别的时间变数找到一个 $ 最多 0-1 时间匹配 。 我们首先证明, 问题在于为根根的时树叶树 。 我们随后建议, $ (n) 美元 美元 美元 美元, 美元 美元 美元 的数值 值 = 直径 。