We present a probabilistic framework for overlapping community discovery and
link prediction for relational data, given as a graph. The proposed framework
has: (1) a deep architecture which enables us to infer multiple layers of
latent features/communities for each node, providing superior link prediction
performance on more complex networks and better interpretability of the latent
features; and (2) a regression model which allows directly conditioning the
node latent features on the side information available in form of node
attributes. Our framework handles both (1) and (2) via a clean, unified model,
which enjoys full local conjugacy via data augmentation, and facilitates
efficient inference via closed form Gibbs sampling. Moreover, inference cost
scales in the number of edges which is attractive for massive but sparse
networks. Our framework is also easily extendable to model weighted networks
with count-valued edges. We compare with various state-of-the-art methods and
report results, both quantitative and qualitative, on several benchmark data
sets.

提出一种基于概率框架的深度学习架构，可用于发现重叠社区和关系数据中的链接预测，利用多层潜在特征 / 社区来提高复杂网络上的链接预测性能，通过回归模型将可用的节点属性作为侧面信息，能更好地解释潜在特征，具有高效且可扩展的优点。