ACRONYM: Augmented Degree Corrected, Community Reticulated Organized Network Yielding Model
提出一种生成和估计无权网络的新方法，能描述更广泛的网络结构，包括不同子网节点通过多种连接机制形成的灵活社区，并利用似然估计改进节点特征估计。
Modeling networks can serve as a means of summarizing high-dimensional complex systems. Adapting an approach devised for dense, weighted networks, we propose a new method for generating and estimating unweighted networks. This approach can describe a broader class of potential networks than existing models, including those where nodes in different subnetworks connect to one another via various attachment mechanisms, inducing flexible and varied community structures. While unweighted edges provide less resolution than continuous weights, restricting to the binary case permits the use of likelihood-based estimation techniques, which can improve estimation of nodal features. The extra flexibility may contribute a different understanding of network generating structures, particularly for networks with heterogeneous densities in different regions. Supplemental appendices and code for this article are available online.