A spike-and-slab prior for dimension selection in generalized linear network eigenmodels
广义线性网络特征模型中用于维度选择的尖峰与厚尾先验
Summary Latent space models are often used to model network data by embedding a network’s nodes into a low-dimensional latent space; however, choosing the dimension of this space remains a challenge. To this end, we begin by formalizing a class of latent space models we call generalized linear network eigenmodels that can model various edge types (binary, ordinal, nonnegative continuous) found in scientific applications. This model class subsumes the traditional eigenmodel…