网络形成模型中潜在社区的检测
Detecting Latent Communities in Network Formation Models
摘要：
本文提出了一个logistic无向网络形成模型，该模型允许对观察到的个体特征进行选择性匹配，并存在边向固定效应。我们将观测特征的系数建模为具有潜在的社区结构，将边缘方向的固定效应建模为低秩。我们提出了一个多步骤的估计过程，涉及核范数正则化，样本分裂，迭代逻辑回归和谱聚类检测潜在的社区。我们发现，当网络的期望度为log n或更高时，可以准确地恢复潜在社区，其中n是网络中的节点数。通过模拟和真实数据集说明了新的估计和推断方法的有限样本性能。
英文摘要：
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.
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。
