优化时间图连接标签,用最少时间戳实现全连通。
Approximating Optimal Labelings for Temporal Connectivity
- 设计时间标签调度策略,使任意两点在时限内连通且标签数最少。
- 证明当 a≥2 时无法优于 O(log n) 的近似比,a≥3 时更差。
- 算法逼近理论极限,关键依赖于 a 与图直径的关系。
在时间图中,边集随时间动态变化,每条边关联一组时间标签,表示其可用的时间步。若存在路径,且边按标签递增顺序遍历,则两点连通。本文研究如何调度边的可用时间,使得所有顶点对在最大允许时间 $a$ 内连通,同时最小化总标签数。该问题称为最小老化标签(MAL),在物流、分发调度和社交网络信息传播中有重要应用,能显著降低基础设施成本、燃料消耗或温室气体排放。此前已知 MAL 在无向图上为 NP 完全,在有向图上为 ext{APX}-hard。本文进一步揭示:当 $a/geq 2$ 时,除非 $ ext{P} = ext{NP}$,否则无法在 $O("log n$) 因子内近似;当 $a/geq 3$ 时,除非 $ ext{NP}subseteq ext{DTIME}(2^{ ext{polylog}(n)})$,否则无法在 $2^{ ext{log}^{1- ext{ε}} n}$ 因子内近似,其中 $n$ 为顶点数。我们提出一组近似算法,在特定条件下几乎达到这些下界。特别地,近似性能取决于 $a$ 与输入图直径的关系。此外,建立了与静态图基础优化问题直径约束生成树(DCSS)的联系,并证明我们的硬性结果也适用于 DCSS。
原文摘要 · Abstract (English)
In a temporal graph the edge set dynamically changes over time according to a set of time-labels associated with each edge that indicates at which time-steps the edge is available. Two vertices are connected if there is a path connecting them in which the edges are traversed in increasing order of their labels. We study the problem of scheduling the availability time of the edges of a temporal graph in such a way that all pairs of vertices are connected within a given maximum allowed time $a$ and the overall number of labels is minimized. The problem, known as \emph{Minimum Aged Labeling} (MAL), has several applications in logistics, distribution scheduling, and information spreading in social networks, where carefully choosing the time-labels can significantly reduce infrastructure costs, fuel consumption, or greenhouse gases. The problem MAL has previously been proved to be NP-complete on undirected graphs and \APX-hard on directed graphs. In this paper, we extend our knowledge on the complexity and approximability of MAL in several directions. We first show that the problem cannot be approximated within a factor better than $O(\log n)$ when $a\geq 2$, unless $\text{P} = \text{NP}$, and a factor better than $2^{\log ^{1-ε} n}$ when $a\geq 3$, unless $\text{NP}\subseteq \text{DTIME}(2^{\text{polylog}(n)})$, where $n$ is the number of vertices in the graph. Then we give a set of approximation algorithms that, under some conditions, almost match these lower bounds. In particular, we show that the approximation depends on a relation between $a$ and the diameter of the input graph. We further establish a connection with a foundational optimization problem on static graphs called \emph{Diameter Constrained Spanning Subgraph} (DCSS) and show that our hardness results also apply to DCSS.
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