  In recent years, inductive graph embedding models, \emph{viz.}, graph neural
networks (GNNs) have become increasingly accurate at link prediction (LP) in
online social networks. The performance of such networks depends strongly on
the input node features, which vary across networks and applications. Selecting
appropriate node features remains application-dependent and generally an open
question. Moreover, owing to privacy and ethical issues, use of personalized
node features is often restricted. In fact, many publicly available data from
online social network do not contain any node features (e.g., demography). In
this work, we provide a comprehensive experimental analysis which shows that
harnessing a transductive technique (e.g., Node2Vec) for obtaining initial node
representations, after which an inductive node embedding technique takes over,
leads to substantial improvements in link prediction accuracy. We demonstrate
that, for a wide variety of GNN variants, node representation vectors obtained
from Node2Vec serve as high quality input features to GNNs, thereby improving
LP performance.
