The Discrepancy of Shortest Paths

التفاصيل البيبلوغرافية
العنوان: The Discrepancy of Shortest Paths
المؤلفون: Bodwin, Greg, Deng, Chengyuan, Gao, Jie, Hoppenworth, Gary, Upadhyay, Jalaj, Wang, Chen
سنة النشر: 2024
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Data Structures and Algorithms
الوصف: The hereditary discrepancy of a set system is a certain quantitative measure of the pseudorandom properties of the system. Roughly, hereditary discrepancy measures how well one can $2$-color the elements of the system so that each set contains approximately the same number of elements of each color. Hereditary discrepancy has well-studied applications e.g. in communication complexity and derandomization. More recently, the hereditary discrepancy of set systems of shortest paths has found applications in differential privacy [Chen et al.~SODA 23]. The contribution of this paper is to improve the upper and lower bounds on the hereditary discrepancy of set systems of unique shortest paths in graphs. In particular, we show that any system of unique shortest paths in an undirected weighted graph has hereditary discrepancy $\widetilde{O}(n^{1/4})$, and we construct lower bound examples demonstrating that this bound is tight up to hidden $\text{polylog } n$ factors. Our lower bounds apply even in the planar and bipartite settings, and they improve on a previous lower bound of $\Omega(n^{1/6})$ obtained by applying the trace bound of Chazelle and Lvov [SoCG'00] to a classical point-line system of Erd\H{o}s. As applications, we improve the lower bound on the additive error for differentially-private all pairs shortest distances from $\Omega(n^{1/6})$ [Chen et al.~SODA 23] to $\Omega(n^{1/4})$, and we improve the lower bound on additive error for the differentially-private all sets range queries problem to $\Omega(n^{1/4})$, which is tight up to hidden $\text{polylog } n$ factors [Deng et al.~WADS 23].
نوع الوثيقة: Working Paper
الوصول الحر: http://arxiv.org/abs/2401.15781Test
رقم الانضمام: edsarx.2401.15781
قاعدة البيانات: arXiv