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arXiv 0704.3293math.PRcond-mat.stat-mechmath.CO
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随机图上模型的重构

Reconstruction for models on random graphs

Antoine Gerschenfeld, Andrea Montanari

摘要:

考虑一个附着在图的顶点上的随机变量的集合。重建问题需要估计其中一个给定的“遥远”的观察。当它们的联合概率分布是关于树的马尔可夫时,几个理论结果(和简单算法)是可用的。在本文中,我们考虑的情况下,序列的随机图,局部收敛到树。特别是,我们开发了一个充分条件的树和图的重建问题相吻合。我们把这个条件应用到随机图的着色上。此外,我们的伊辛模型在这样的图形的行为特征,无论是有吸引力的和随机的相互作用(分别为“铁磁”和“自旋玻璃”)。

英文摘要:

Consider a collection of random variables attached to the vertices of a graph. The reconstruction problem requires to estimate one of them given `far away' observations. Several theoretical results (and simple algorithms) are available when their joint probability distribution is Markov with respect to a tree. In this paper we consider the case of sequences of random graphs that converge locally to trees. In particular, we develop a sufficient condition for the tree and graph reconstruction problem to coincide. We apply such condition to colorings of random graphs. Further, we characterize the behavior of Ising models on such graphs, both with attractive and random interactions (respectively, `ferromagnetic' and `spin glass').

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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