  Node classification and link prediction are widely studied in graph
representation learning. While both transductive node classification and link
prediction operate over a single input graph, they have so far been studied
separately. Node classification models take an input graph with node features
and incomplete node labels, and implicitly assume that the graph is
relationally complete, i.e., no edges are missing. By contrast, link prediction
models are solely motivated by relational incompleteness of the input graphs,
and do not typically leverage node features or classes. We propose a unifying
perspective and study the problems of (i) transductive node classification over
incomplete graphs and (ii) link prediction over graphs with node features,
introduce a new dataset for this setting, WikiAlumni, and conduct an extensive
benchmarking study.
