The Network Disturbance Model of Doreian (1989) expresses the dependency between observations taken at the vertices of a network by modelling the correlation between neighbouring vertices, using a single correlation parameter $\rho$. It has been observed that estimation of $\rho$ in dense graphs, using the method of Maximum Likelihood, leads to results that can be both biased and very unstable. In this paper, we sketch why this is the case, showing that the variability cannot be avoided, no matter how large the network. We also propose a more intuitive estimator of $\rho$, which shows little bias. The related Network Effects Model is briefly discussed.

翻译：Doreian 网络扰动模型(1989年)表示,在网络顶端通过模拟相邻的顶端之间的相关性,使用单一的相关参数$rho$来模拟相邻的顶端之间的关联性,从而在网络顶端进行的观测之间具有依赖性。据观察,使用最大可能性方法在密度图形中估计$\rho$会导致既具有偏向性又非常不稳定的结果。在本文中,我们勾画出为什么情况如此,表明无论网络有多大,这种变异性都无法避免。我们还提出了一个更直观的$\rho$估算器,它显示了很少的偏差。我们简要讨论了相关的网络效果模型。