  The task of graph node classification is often approached by utilizing a
local Graph Neural Network (GNN), that learns only local information from the
node input features and their adjacency. In this paper, we propose to improve
the performance of node classification GNNs by utilizing both global and local
information, specifically by learning label- and node- features. We therefore
call our method Global-Local-GNN (GLGNN). To learn proper label features, for
each label, we maximize the similarity between its features and nodes features
that belong to the label, while maximizing the distance between nodes that do
not belong to the considered label. We then use the learnt label features to
predict the node classification map. We demonstrate our GLGNN using three
different GNN backbones, and show that our approach improves baseline
performance, revealing the importance of global information utilization for
node classification.
