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首页> 外文期刊>Computational complexity >ON THE CHARACTERIZATION OF 1-SIDED ERROR STRONGLY TESTABLE GRAPH PROPERTIES FOR BOUNDED-DEGREE GRAPHS
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ON THE CHARACTERIZATION OF 1-SIDED ERROR STRONGLY TESTABLE GRAPH PROPERTIES FOR BOUNDED-DEGREE GRAPHS

机译:关于界限图的单面误差强可测试的图形属性的表征

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We study property testing of (di)graph properties in bounded-degree graph models. The study of graph properties in bounded-degree models is one of the focal directions of research in property testing in the last 15 years. However, despite the many results and the extensive research effort, there is no characterization of the properties that are strongly testable (i.e. testable with constant query complexity) even for 1-sided error tests. The bounded-degree model can naturally be generalized to directed graphs resulting in two models that were considered in the literature. The first contains the directed graphs in which the out-degree is bounded but the in-degree is not restricted. In the other, both the out-degree and in-degree are bounded. We give a characterization of the 1-sided error strongly testable monotone graph properties and the 1-sided error strongly testable hereditary graph properties in all the bounded-degree directed and undirected graphs models.
机译:我们研究界限图模型中(DI)图形属性的物业测试。界限模型中的图形属性研究是过去15年来物业测试研究的局部方向之一。然而,尽管有许多结果和广泛的研究工作,但也没有强烈可测试的性质的表征(即,通过恒定查询复杂性可测试)即使是针对单侧的错误测试也是如此。界限模型可以自然地推广到导致的图形,导致文献中考虑的两个模型。第一个包含所指示的图形,其中OUT度被界定,但不限于程度。另一方面,介乎偏移度和程度都是有界的。我们表征了单面误差强可验证的单调图属性,并且单面误差在所有有界度指导和无向图​​形模型中的遗传性图形属性强。

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