  Document network embedding aims at learning representations for a structured
text corpus i.e. when documents are linked to each other. Recent algorithms
extend network embedding approaches by incorporating the text content
associated with the nodes in their formulations. In most cases, it is hard to
interpret the learned representations. Moreover, little importance is given to
the generalization to new documents that are not observed within the network.
In this paper, we propose an interpretable and inductive document network
embedding method. We introduce a novel mechanism, the Topic-Word Attention
(TWA), that generates document representations based on the interplay between
word and topic representations. We train these word and topic vectors through
our general model, Inductive Document Network Embedding (IDNE), by leveraging
the connections in the document network. Quantitative evaluations show that our
approach achieves state-of-the-art performance on various networks and we
qualitatively show that our model produces meaningful and interpretable
representations of the words, topics and documents.
