Many scientific and engineering fields involve analyzing network data. For
document networks, relational topic models (RTMs) provide a probabilistic
generative process to describe both the link structure and document contents,
and they have shown promise on predicting network structures and discovering
latent topic representations. However, existing RTMs have limitations in both
the restricted model expressiveness and incapability of dealing with imbalanced
network data. To expand the scope and improve the inference accuracy of RTMs,
this paper presents three extensions: 1) unlike the common link likelihood with
a diagonal weight matrix that allows the-same-topic interactions only, we
generalize it to use a full weight matrix that captures all pairwise topic
interactions and is applicable to asymmetric networks; 2) instead of doing
standard Bayesian inference, we perform regularized Bayesian inference
(RegBayes) with a regularization parameter to deal with the imbalanced link
structure issue in common real networks and improve the discriminative ability
of learned latent representations; and 3) instead of doing variational
approximation with strict mean-field assumptions, we present collapsed Gibbs
sampling algorithms for the generalized relational topic models by exploring
data augmentation without making restricting assumptions. Under the generic
RegBayes framework, we carefully investigate two popular discriminative loss
functions, namely, the logistic log-loss and the max-margin hinge loss.
Experimental results on several real network datasets demonstrate the
significance of these extensions on improving the prediction performance, and
the time efficiency can be dramatically improved with a simple fast
approximation method.

通过三个扩展来改进 RTMs 模型，扩大模型表达性和处理不平衡网络结构的能力，实现更加精准的推理和主题发现.