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The Complexity of Approximating Vertex Expansion

机译:近似顶点扩展的复杂度

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We study the complexity of approximating the vertex expansion of graphs G = (V, E), defined as φV def = minSCV n . |N(S)|/(|S||V S). We give a simple polynomialtime algorithm for finding a subset with vertex expansion O(&root;φVlog d) where d is the maximum degree of the graph. Our main result is an asymptotically matching lower bound: under the Small Set Expansion (SSE) hypothesis, it is hard to find a subset with expansion less than C(&root;φVlog d) for an absolute constant C. In particular, this implies for all constant "≥ &epsil; > 0, it is SSE-hard to distinguish whether the vertex expansion ≤ ε or at least an absolute constant. The analogous threshold for edge expansion is &root; φ with no dependence on the degree (Here φ denotes the optimal edge expansion). Thus our results suggest that vertex expansion is harder to approximate than edge expansion. In particular, while Cheeger's algorithm can certify constant edge expansion, it is SSE-hard to certify constant vertex expansion in graphs.
机译:我们研究了近似图G =(V,E)的顶点扩展的复杂度,定义为φVdef = minSCV n。 | N(S)| /(|| S || VS)。我们给出了一个简单的多项式时间算法,用于找到顶点扩展为O(&root;φVlogd)的子集,其中d是图的最大程度。我们的主要结果是渐近匹配的下界:在小集扩展(SSE)假设下,对于绝对常数C,很难找到扩展小于C(&root;φVlogd)的子集。特别是,这意味着如果所有常数都≥≥0,则很难区分顶点扩展≤ε还是至少是一个绝对常数。边缘扩展的类似阈值是&root;φ,与度无关(此处φ表示因此,我们的结果表明顶点扩展比边缘扩展更难近似,特别是,尽管Cheeger算法可以证明恒定的边缘扩展,但是要证明图中恒定的顶点扩展却很难通过SSE进行。

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