We initiate the study on fault-tolerant spanners in hypergraphs and develop fast algorithms for their constructions. A fault-tolerant (FT) spanner preserves approximate distances under network failures, often used in applications like network design and distributed systems. While classic (fault-free) spanners are believed to be easily extended to hypergraphs such as by the method of associated graphs, we reveal that this is not the case in the fault-tolerant setting: simple methods can only get a linear size in the maximum number of faults $f$. In contrast, all known optimal size of FT spanners are sublinear in $f$. Inspired by the FT clustering technique, we propose a clustering based algorithm that achieves an improved sublinear size bound. For an $n$-node $m$-edge hypergraph with rank $r$ and a sketch parameter $k$, our algorithm constructs edge FT (EFT) hyperspanners of stretch $2k-1$ and size $O(k^2f^{1-1/(rk)}n^{1+1/k}\log n)$ with high probability in time $\widetilde{O}(mr^3+fn)$. We also establish a lower bound of $Ω(f^{1-1/r-1/rk}n^{1+1/k-o(1)})$ edges for EFT hyperspanners, which leaves a gap of poly$(k)f^{1/r}$. Finally, we provide an algorithm for constructing additive EFT hyperspanners by combining multiplicative EFT hyperspanners with additive hyperspanners. We believe that our work will spark interest in developing optimal FT spanners for hypergraphs.
翻译:我们首次研究了超图中的容错稀疏生成子图,并开发了其构造的快速算法。容错稀疏生成子图在网络故障下保持近似距离,常用于网络设计和分布式系统等应用。虽然经典(无故障)稀疏生成子图被认为可通过关联图等方法轻松推广至超图,但我们揭示了在容错设置下并非如此:简单方法仅能在最大故障数 $f$ 下获得线性规模。相比之下,所有已知的容错稀疏生成子图最优规模均为 $f$ 的亚线性。受容错聚类技术启发,我们提出了一种基于聚类的算法,实现了改进的亚线性规模边界。对于具有秩 $r$ 和草图参数 $k$ 的 $n$ 节点 $m$ 边超图,我们的算法以高概率在 $\widetilde{O}(mr^3+fn)$ 时间内构造出拉伸度为 $2k-1$、规模为 $O(k^2f^{1-1/(rk)}n^{1+1/k}\log n)$ 的边容错超稀疏生成子图。我们还建立了边容错超稀疏生成子图 $Ω(f^{1-1/r-1/rk}n^{1+1/k-o(1)})$ 边的下界,这留下了 poly$(k)f^{1/r}$ 的间隙。最后,我们通过结合乘法边容错超稀疏生成子图与加法超稀疏生成子图,提供了构造加法边容错超稀疏生成子图的算法。我们相信,我们的工作将激发开发超图最优容错稀疏生成子图的兴趣。