A joint degree matrix (JDM) specifies the number of connections between nodes of given degrees in a graph, for all degree pairs and uniquely determines the degree sequence of the graph. We consider the space of all balanced realizations of an arbitrary JDM, realizations in which the links between any two degree groups are placed as uniformly as possible. We prove that a swap Markov Chain Monte Carlo (MCMC) algorithm in the space of all balanced realizations of an {\em arbitrary} graphical JDM mixes rapidly, i.e., the relaxation time of the chain is bounded from above by a polynomial in the number of nodes $n$. To prove fast mixing, we first prove a general factorization theorem similar to the Martin-Randall method for disjoint decompositions (partitions). This theorem can be used to bound from below the spectral gap with the help of fast mixing subchains within every partition and a bound on an auxiliary Markov chain between the partitions. Our proof of the general factorization theorem is direct and uses conductance based methods (Cheeger inequality).
翻译:联合学位矩阵( JDM ) 在图形中指定给定度节点之间的连接次数, 用于所有度对等, 并独有地决定图形的度序列 。 我们考虑任意 JDM 的所有均衡实现空间, 即任何两个等级组之间的联系尽可能一致。 我们证明, 在 \ 任意 图形 JDM 组合的所有均衡实现空间中, Markov 链 Monte Carlo (MC ) 的转换算法( MC ), 即链节点的放松时间从上方被一个点点数的多数值( $ $ ) 捆绑。 为了证明快速混合, 我们首先证明一个类似于 Martin- Randall 断交法( partments) 的一般性因子集分解参数( partmentations) 。 这个参数可以用来从光谱差距下绑定, 帮助在每个分区内快速混合子链, 并绑定在分区之间的辅助 Markov 链 。 我们关于一般因子化 的证明是直接的, 并使用基于 方法 ( Cheger 不平等 ) 。