  Searching through networks of documents is an important task. A promising
path to improve the performance of information retrieval systems in this
context is to leverage dense node and content representations learned with
embedding techniques. However, these techniques cannot learn representations
for documents that are either isolated or whose content is missing. To tackle
this issue, assuming that the topology of the network and the content of the
documents correlate, we propose to estimate the missing node representations
from the available content representations, and conversely. Inspired by recent
advances in machine translation, we detail in this paper how to learn a linear
transformation from a set of aligned content and node representations. The
projection matrix is efficiently calculated in terms of the singular value
decomposition. The usefulness of the proposed method is highlighted by the
improved ability to predict the neighborhood of nodes whose links are
unobserved based on the projected content representations, and to retrieve
similar documents when content is missing, based on the projected node
representations.
