arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 0911.0600math.COmath.PR
收藏

具有独立边的随机图的邻接矩阵和Laplacian的集中性

Concentration of the adjacency matrix and of the Laplacian in random graphs with independent edges

Roberto Imbuzeiro Oliveira

摘要:

考虑任何随机图模型,其中潜在的边缘独立出现,可能具有不同的概率,并假设最小期望度为omega(ln n)。证明了该随机图的邻接矩阵和Laplacian集中在边权为随机模型中概率的加权图的相应矩阵周围。虽然这似乎令人惊讶,但我们将看到这种矩阵集中现象是关于Erös-Rényi模型的已知结果的推广。特别是,我们将认为,矩阵浓度是隐含的准随机图形属性的理论。我们提出了两个主要的应用程序的主要结果。在图G上的键渗流中,我们证明了随机子图的拉普拉斯算子通常非常接近于G的拉普拉斯算子。作为一个推论,我们改进了由于钟和霍恩,是通过更复杂的方法得出的光谱间隙的界限。在非齐次随机图中,存在点X_1,...,X_n均匀分布在区间[0,1]上,每对以概率p κ(X_i,X_j)连通。证明了若\ln n/n<< p<< 1且kappa有界,则随机图的邻接矩阵接近于一个用kappa定义的积分算子.我们的主要证明工具是一个新的矩阵鞅的浓度不等式,推广弗里德曼不等式的标准标量设置。

英文摘要:

Consider any random graph model where potential edges appear independently, with possibly different probabilities, and assume that the minimum expected degree is omega(ln n). We prove that the adjacency matrix and the Laplacian of that random graph are concentrated around the corresponding matrices of the weighted graph whose edge weights are the probabilities in the random model. While this may seem surprising, we will see that this matrix concentration phenomenon is a generalization of known results about the Erös-Rényi model. In particular, we will argue that matrix concentration is implicit the theory of quasi-random graph properties. We present two main applications of the main result. In bond percolation over a graph G, we show that the Laplacian of the random subgraph is typically very close to the Laplacian of G. As a corollary, we improve upon a bound for the spectral gap due to Chung and Horn that was derived via much more complicated methods. In inhomogeneous random graphs, there are points X_1,...,X_n uniformly distributed on the interval [0,1] and each pair is connected with probability p kappa(X_i,X_j). We show that if \ln n/n<< p<< 1 and kappa is bounded, then the adjacency matrix of the random graph is close to an integral operator defined in terms of kappa. Our main proof tool is a new concentration inequality for matrix martingales that generalizes Freedman's inequality for the standard scalar setting.

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。

↑