  Topic models are frequently used in machine learning owing to their high
interpretability and modular structure. However, extending a topic model to
include a supervisory signal, to incorporate pre-trained word embedding vectors
and to include a nonlinear output function is not an easy task because one has
to resort to a highly intricate approximate inference procedure. The present
paper shows that topic modeling with pre-trained word embedding vectors can be
viewed as implementing a neighborhood aggregation algorithm where messages are
passed through a network defined over words. From the network view of topic
models, nodes correspond to words in a document and edges correspond to either
a relationship describing co-occurring words in a document or a relationship
describing the same word in the corpus. The network view allows us to extend
the model to include supervisory signals, incorporate pre-trained word
embedding vectors and include a nonlinear output function in a simple manner.
In experiments, we show that our approach outperforms the state-of-the-art
supervised Latent Dirichlet Allocation implementation in terms of held-out
document classification tasks.
