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Testing triangle-freeness in general graphs

机译:测试常规图中的无三角形

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In this paper we consider the problem of testing whether a graph is triangle-free, and more generally, whether it is H-free, for a fixed subgraph H. The algorithm should accept graphs that are triangle-free and reject graphs that are far from being triangle-free in the sense that a constant fraction of the edges should be removed in order to obtain a triangle-free graph. The algorithm is allowed a small probability of error.This problem has been studied quite extensively in the past, but the focus was on dense graphs, that is, when d = Θ(n), where d is the average degree in the graph and n is the number of vertices. Here we study the complexity of the problem in general graphs, that is, for varying d.Our main finding is a lower bound of Ω(n1/3) on the necessary number of queries that holds for every d n1-ν(n), where ν(n) = o(1). Since when d = Θ(n) the number of queries sufficient for testing has been known to be independent of n, we observe an abrupt, threshold-like behavior of the complexity of testing around n. This lower bound holds for testing H-freeness of every non-bipartite subgraph H.Additionally we provide sub-linear upper bounds for testing triangle-freeness that are at most quadratic in the stated lower bounds, and we describe a transformation from certain one-sided error lower bounds for testing subgraph-freeness to two-sided error lower bounds.Finally, in the course of our analysis we show that dense random Cayley graphs behave like quasi-random graphs in the sense that relatively large subsets of vertices have the "correct" edge density. The result for subsets of this size cannot be obtained from the known spectral techniques that only supply such estimates for much larger subsets.
机译:在本文中,我们考虑测试固定子图 H 的图形是否无三角形,更一般地说,是否无 H 的问题。该算法应接受无三角形的图形,并拒绝远离无三角形的图形,因为必须去除一定比例的边缘以获得无三角形的图形。该算法允许出现错误的可能性很小。过去已经对该问题进行了广泛的研究,但重点是密集图,即 d =Θ( n ),其中 d 是图中的平均度,而 n 是顶点数。在这里,我们研究一般图形中问题的复杂性,即对于变化的 d 。我们的主要发现是Ω( n 1/3 )来确定满足每d < n 1-ν( n SUP>,其中ν( n )= o (1)。由于已知当 d =Θ( n )时,足以进行测试的查询数量与 n 不相关,因此我们观察到了突然的情况,类似于 n 阈值型行为测试的复杂性。此下限适用于测试每个非二分图 H H -自由度。此外,我们提供了用于测试三角形自由度的子线性上限,该上限最大为二次方。规定的下限,我们描述了从测试单图自由度的某些单侧误差下限到双侧误差下限的转换。最后,在我们的分析过程中,我们表明,密集的随机Cayley图的行为类似于准相对较大的顶点子集具有“正确的”边缘密度的意义上的随机图。这种大小的子集的结果无法从已知的光谱技术中获得,后者只能为更大的子集提供此类估计。

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