一个随机图移位启用的可能性有多大？
How likely is a random graph shift-enabled?
摘要：
底层图的移位启用属性在设计分布式过滤器时是必不可少的。这篇文章讨论了一个随机图何时启用移位。特别是，流行的图模型ER，WS，BA随机图，加权和未加权，以及有符号图。我们的研究结果表明，所考虑的未加权连通随机图的移位启用的概率很高，当边的数量是适度的高。然而，非常密集的图，以及全连接的图，是不能移位的。有趣的是，在加权连通图中没有观察到这种行为，加权连通图总是启用移位，除非图中的边数非常低。
英文摘要：
The shift-enabled property of an underlying graph is essential in designing distributed filters. This article discusses when a random graph is shift-enabled. In particular, popular graph models ER, WS, BA random graph are used, weighted and unweighted, as well as signed graphs. Our results show that the considered unweighted connected random graphs are shift-enabled with high probability when the number of edges is moderately high. However, very dense graphs, as well as fully connected graphs, are not shift-enabled. Interestingly, this behaviour is not observed for weighted connected graphs, which are always shift-enabled unless the number of edges in the graph is very low.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
