  Many important problems can be formulated as reasoning in knowledge graphs.
Representation learning has proved extremely effective for transductive
reasoning, in which one needs to make new predictions for already observed
entities. This is true for both attributed graphs(where each entity has an
initial feature vector) and non-attributed graphs (where the only initial
information derives from known relations with other entities). For
out-of-sample reasoning, where one needs to make predictions for entities that
were unseen at training time, much prior work considers attributed graph.
However, this problem is surprisingly under-explored for non-attributed graphs.
In this paper, we study the out-of-sample representation learning problem for
non-attributed knowledge graphs, create benchmark datasets for this task,
develop several models and baselines, and provide empirical analyses and
comparisons of the proposed models and baselines.
