  Textual network embeddings aim to learn a low-dimensional representation for
every node in the network so that both the structural and textual information
from the networks can be well preserved in the representations. Traditionally,
the structural and textual embeddings were learned by models that rarely take
the mutual influences between them into account. In this paper, a deep neural
architecture is proposed to effectively fuse the two kinds of informations into
one representation. The novelties of the proposed architecture are manifested
in the aspects of a newly defined objective function, the complementary
information fusion method for structural and textual features, and the mutual
gate mechanism for textual feature extraction. Experimental results show that
the proposed model outperforms the comparing methods on all three datasets.
