论文标题
检测网络形成模型中的潜在社区
Detecting Latent Communities in Network Formation Models
论文作者
论文摘要
本文提出了一个逻辑无向网络形成模型，该模型允许在观察到的个体特征和边缘固定效果的存在上进行分类匹配。我们对观察到的特征的系数进行建模，以具有潜在的社区结构和边缘固定效应，为低等级。我们提出了一个多步估计程序，涉及核规范正规化，样品分裂，迭代逻辑回归和光谱聚类以检测潜在社区。我们表明，当网络的预期度为log n或更高时，可以准确恢复潜在社区，其中n是网络中的节点数量。通过模拟和真实数据集说明了新估计和推理方法的有限样本性能。
This paper proposes a logistic undirected network formation model which allows for assortative matching on observed individual characteristics and the presence of edge-wise fixed effects. We model the coefficients of observed characteristics to have a latent community structure and the edge-wise fixed effects to be of low rank. We propose a multi-step estimation procedure involving nuclear norm regularization, sample splitting, iterative logistic regression and spectral clustering to detect the latent communities. We show that the latent communities can be exactly recovered when the expected degree of the network is of order log n or higher, where n is the number of nodes in the network. The finite sample performance of the new estimation and inference methods is illustrated through both simulated and real datasets.
