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arXiv 0912.0685cs.DMcs.DS
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具有固定度序列的无向和有向图的均匀抽样

Uniform sampling of undirected and directed graphs with a fixed degree sequence

Annabell Berger, Matthias Müller-Hannemann

摘要:

网络分析中的许多应用需要算法从具有指定度序列的所有图的集合中随机均匀地采样。我们提出了一个基于马尔可夫链的方法,收敛到均匀分布的所有实现的有向和无向的情况下。这些马尔可夫链是否快速混合仍然是一个公开的挑战。对于有向图的情况下,我们还解释了在本文中,一个流行的切换算法一般不能均匀随机采样,因为马尔可夫链的状态图分解成不同的同构组件。我们把状态图是强连通的度序列称为弧交换序列。为了处理任意度序列,我们开发了两种不同的解决方案。第一个使用一个额外的操作(诱导定向3-循环的重定向),这使得状态图强连接,第二个随机选择的同构组件和样本之一,我们的主要贡献是一个精确的特征弧交换序列,导致一个有效的识别算法。最后,我们指出了一些有趣的后果,网络分析。

英文摘要:

Many applications in network analysis require algorithms to sample uniformly at random from the set of all graphs with a prescribed degree sequence. We present a Markov chain based approach which converges to the uniform distribution of all realizations for both the directed and undirected case. It remains an open challenge whether these Markov chains are rapidly mixing. For the case of directed graphs, we also explain in this paper that a popular switching algorithm fails in general to sample uniformly at random because the state graph of the Markov chain decomposes into different isomorphic components. We call degree sequences for which the state graph is strongly connected arc swap sequences. To handle arbitrary degree sequences, we develop two different solutions. The first uses an additional operation (a reorientation of induced directed 3-cycles) which makes the state graph strongly connected, the second selects randomly one of the isomorphic components and samples inside it. Our main contribution is a precise characterization of arc swap sequences, leading to an efficient recognition algorithm. Finally, we point out some interesting consequences for network analysis.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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