  A network embedding consists of a vector representation for each node in the
network. Its usefulness has been shown in many real-world application domains,
such as social networks and web networks. Directed networks with text
associated with each node, such as software package dependency networks, are
commonplace. However, to the best of our knowledge, their embeddings have
hitherto not been specifically studied. In this paper, we propose PCTADW-1 and
PCTADW-2, two algorithms based on neural networks that learn embeddings of
directed networks with text associated with each node. We create two new
node-labeled such networks: The package dependency networks in two popular
GNU/Linux distributions, Debian and Fedora. We experimentally demonstrate that
the embeddings produced by our algorithms resulted in node classification with
better quality than those of various baselines on these two networks. We
observe that there exist systematic presence of analogies (similar to those in
word embeddings) in the network embeddings of software package dependency
networks. To the best of our knowledge, this is the first time that such
systematic presence of analogies is observed in network and document
embeddings. We further demonstrate that these network embeddings can be novelly
used for better understanding software attributes, such as the development
process and user interface of software, etc.
