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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是多么努力?

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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:(大约)计算图中三角形数量的问题是图论中的基本问题之一。在本文中,我们研究流模型中的问题。具体地,随机算法来解决这个问题的内存量。在允许算法在流上允许算法的情况下,我们为具有M边缘的图表G提供最佳可能的下限ω(m)。如果允许恒定数量的通过,我们显示ω(m / t)的下限,t的三角形。在某种意义上,我们匹配的是,使用2-pass O(m / t〜(1/3)) - 内存算法 - 内存算法,解决了与至少与t三角形的图形不同三角形的图形的问题。我们提出了一种新的图表参数ρ(g) - 三角形密度,并猜测三角形问题的空间复杂度是θ&(m /ρ(g))。我们通过第二种算法匹配这一点,该算法解决了使用O(m /ρ(g)) - 存储器的区别问题。

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