论文标题

测试子图统计的线性不平等现象

Testing linear inequalities of subgraph statistics

论文作者

Gishboliner, Lior, Shapira, Asaf, Stagni, Henrique

论文摘要

属性测试仪是快速的随机算法,其任务是区分满足某些预定属性$ {\ cal p} $的输入和远离满足它的输入。由于这些算法通过检查输入的一小部分随机部分来运行,因此人们希望测试的最自然属性是输入是否不包含某些禁止的小型子结构。在图形的设置中,Alon等人获得了这样的结果,他证明,对于任何有限的图族$ {\ cal f} $,被诱导的$ {\ cal f} $ - 免费(即未包含{\ cal f} $)的任何$ f \)的属性。 自然要问一个人是否可以进一步迈进,并证明还可以测试涉及诱导子图的更精细的特性。 Alon等人结果的一种概括。是由Goldreich和Shinkar提出的,他们推测,对于任何有限的图表$ {\ cal f} $,以及任何线性不等式涉及{\ cal f} $在输入图中的图形密度$ f \ in {\ cal f} $中的密度,可以在某些限制性的图形模型中测试满足此不平等的属性。本文中我们的主要结果以以下强的形式反驳了这种猜想:即使在Graph属性测试的经典(不受限制)模型中,这种类型的某些属性也无法测试。 证据显着偏离了该领域的先前的不可测量。主要思想是使用与诱导子图密度相关的线性不等式,以编码为quasirandom图的属性。

Property testers are fast randomized algorithms whose task is to distinguish between inputs satisfying some predetermined property ${\cal P}$ and those that are far from satisfying it. Since these algorithms operate by inspecting a small randomly selected portion of the input, the most natural property one would like to be able to test is whether the input does not contain certain forbidden small substructures. In the setting of graphs, such a result was obtained by Alon et al., who proved that for any finite family of graphs ${\cal F}$, the property of being induced ${\cal F}$-free (i.e. not containing an induced copy of any $F \in {\cal F}$) is testable. It is natural to ask if one can go one step further and prove that more elaborate properties involving induced subgraphs are also testable. One such generalization of the result of Alon et al. was formulated by Goldreich and Shinkar who conjectured that for any finite family of graphs ${\cal F}$, and any linear inequality involving the densities of the graphs $F \in {\cal F}$ in the input graph, the property of satisfying this inequality can be tested in a certain restricted model of graph property testing. Our main result in this paper disproves this conjecture in the following strong form: some properties of this type are not testable even in the classical (i.e. unrestricted) model of graph property testing. The proof deviates significantly from prior non-testability results in this area. The main idea is to use a linear inequality relating induced subgraph densities in order to encode the property of being a quasirandom graph.

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