Distributed Approximate k-Core Decomposition and Min-Max Edge Orientation: Breaking the Diameter Barrier

T-H. Hubert Chan, Mauro Sozio, Bintao Sun
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引用次数: 5

Abstract

We design distributed algorithms to compute approximate solutions for several related graph optimization problems. All our algorithms have round complexity being logarithmic in the number of nodes of the underlying graph and in particular independent of the graph diameter. By using a primal-dual approach, we develop a 2(1+ε)-approximation algorithm for computing the coreness values of the nodes in the underlying graph, as well as a 2(1+ε)-approximation algorithm for the min-max edge orientation problem, where the goal is to orient the edges so as to minimize the maximum weighted in-degree. We provide lower bounds showing that the aforementioned algorithms are tight both in terms of the approximation guarantee and the round complexity. Finally, motivated by the fact that the densest subset problem has an inherent dependency on the diameter of the graph, we study a weaker version that does not suffer from the same limitation.
分布近似k核分解和最小-最大边缘定向:突破直径障碍
我们设计了分布式算法来计算几个相关图优化问题的近似解。我们所有的算法在底层图的节点数量上都具有对数的圆复杂度,特别是与图的直径无关。通过使用原始对偶方法,我们开发了用于计算底层图中节点核心值的2(1+ε)近似算法,以及用于最小-最大边缘定向问题的2(1+ε)近似算法,其中目标是定向边缘以最小化最大加权in度。我们提供了下界,表明上述算法在近似保证和轮复杂度方面都是严格的。最后,由于最密集子集问题对图的直径具有固有的依赖性,我们研究了一个不受相同限制的较弱版本。
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