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How Hard Is Counting Triangles in the Streaming Model?

机译:在流模型中计算三角形有多难?

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The problem of (approximately) counting the number of triangles in a graph is one of the basic problems in graph theory. In this paper we study the problem in the streaming model. Specifically, the amount of memory required by a randomized algorithm to solve this problem. In case the algorithm is allowed one pass over the stream, we present a best possible lower bound of Ω(m) for graphs G with m edges. If a constant number of passes is allowed, we show a lower bound of Ω(m/T), T the number of triangles. We match, in some sense, this lower bound with a 2-pass O(m/T~(1/3))-memory algorithm that solves the problem of distinguishing graphs with no triangles from graphs with at least T triangles. We present a new graph parameter ρ(G) - the triangle density, and conjecture that the space complexity of the triangles problem is θ&(m/ρ(G)). We match this by a second algorithm that solves the distinguishing problem using O(m/ρ(G))-memory.
机译:(近似)计算图中的三角形数量的问题是图论中的基本问题之一。在本文中,我们研究了流模型中的问题。具体来说,是随机算法解决此问题所需的内存量。如果算法允许流一次通过,则对于具有m条边的图G,我们提出了Ω(m)的最佳可能下界。如果允许通过的次数恒定,我们将显示Ω(m / T)的下界,即三角形的数目T。从某种意义上讲,我们将此下限与2遍O(m / T〜(1/3))内存算法相匹配,该算法解决了区分不包含三角形的图形与包含至少T个三角形的图形的问题。我们提出了一个新的图形参数ρ(G)-三角形密度,并推测三角形问题的空间复杂度为θ&(m /ρ(G))。我们通过使用O(m /ρ(G))内存解决区分问题的第二种算法来匹配它。

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