论文标题
动态图系列的细粒度复杂性下限
Fine-Grained Complexity Lower Bounds for Families of Dynamic Graphs
论文作者
论文摘要
动态图算法是一个数据结构,该数据结构回答有关当前图的属性的查询,同时支持图形修改,例如边缘插入和删除。先前的工作显示了一般动态图的强烈条件下限,但是在实践中出现的图族通常表现出现有下限构建体不具有的结构特性。我们研究了三个特定的图形家族,它们是普遍存在的,即恒定的图形,幂律图和扩展器图,并为它们提供了第一个条件下限。我们的结果表明,即使将我们的注意力限制在这些图形类中之一时,任何针对距离计算或近似或最大匹配等基本图问题的算法也无法同时达到亚物质更新时间和查询时间。例如,我们表明,对于最大匹配,($ s,t $)的一般图与一般图相同的下限 - 在恒定数度图,幂律图或扩展器中的距离。也就是说,在$ m $ - 边缘图中,没有$ o(m^{1/2-ε})$更新时间和$ o(m^{1-ε})$查询时间的动态算法,对于任何小$ε> 0 $。请注意,对于($ s,t $) - 距离微不足道的动态算法达到了恒定更新时间的几乎匹配的上限,而$ o(m)$ query Query时间。我们证明了其他图形家族以及其他基本问题(例如最密集的子图检测和完美匹配)的范围相似。
A dynamic graph algorithm is a data structure that answers queries about a property of the current graph while supporting graph modifications such as edge insertions and deletions. Prior work has shown strong conditional lower bounds for general dynamic graphs, yet graph families that arise in practice often exhibit structural properties that the existing lower bound constructions do not possess. We study three specific graph families that are ubiquitous, namely constant-degree graphs, power-law graphs, and expander graphs, and give the first conditional lower bounds for them. Our results show that even when restricting our attention to one of these graph classes, any algorithm for fundamental graph problems such as distance computation or approximation or maximum matching, cannot simultaneously achieve a sub-polynomial update time and query time. For example, we show that the same lower bounds as for general graphs hold for maximum matching and ($s,t$)-distance in constant-degree graphs, power-law graphs or expanders. Namely, in an $m$-edge graph, there exists no dynamic algorithms with both $O(m^{1/2 - ε})$ update time and $ O(m^{1 -ε})$ query time, for any small $ε> 0$. Note that for ($s,t$)-distance the trivial dynamic algorithm achieves an almost matching upper bound of constant update time and $O(m)$ query time. We prove similar bounds for the other graph families and for other fundamental problems such as densest subgraph detection and perfect matching.