Node Classification Meets Link Prediction on Knowledge Graphs

Node classification and link prediction are widely studied tasks in graph representation learning. While both transductive node classification and link prediction operate over a single input graph, they are studied in isolation so far, which leads to discrepancies. Node classification models take as input a graph with node features and incomplete node labels, and implicitly assume that the input graph is relationally complete, i.e., no edges are missing from the input graph. This is in sharp contrast with link prediction models that are solely motivated by the relational incompleteness of the input graph which does not have any node features. 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. We propose an extension to an existing box embedding model, and show that this model is fully expressive, and can solve both of these tasks in an end-to-end fashion. To empirically evaluate our model, we construct a knowledge graph with node features, which is challenging both for node classification and link prediction. Our model performs very strongly when compared to the respective state-of-the-art models for node classification and link prediction on this dataset and shows the importance of a unified perspective for node classification and link prediction on knowledge graphs.