Providing theoretical guarantees for parameter estimation in exponential random graph models is a largely open problem. While maximum likelihood estimation has theoretical guarantees in principle, verifying the assumptions for these guarantees to hold can be very difficult. Moreover, in complex networks, numerical maximum likelihood estimation is computer-intensive and may not converge in reasonable time. To ameliorate this issue, local dependency exponential random graph models have been introduced, which assume that the network consists of many independent exponential random graphs. In this setting, progress towards maximum likelihood estimation has been made. However the estimation is still computer-intensive. Instead, we propose to use so-called Stein estimators: we use the Stein characterizations to obtain new estimators for local dependency exponential random graph models.

翻译：为指数随机图模型中的参数估计提供理论保证在很大程度上仍是一个开放性问题。虽然极大似然估计在理论上具有可保证性，但验证其成立所需的假设条件往往极为困难。此外，在复杂网络中，数值极大似然估计计算强度高，且可能无法在合理时间内收敛。为解决这一问题，研究者提出了局部依赖指数随机图模型，该模型假设网络由许多相互独立的指数随机图构成。在此设定下，极大似然估计的进展已初见成效，但估计过程仍面临计算负担。作为替代方案，我们提出使用所谓的Stein估计量：借助Stein特征刻画方法为局部依赖指数随机图模型构建新型估计量。