Global-local graph neural networks for node-classification

Moshe Eliasof, Eran Treister

Abstract: Highlights•We propose to learn label features to capture global information of the input graph.•We fuse label and node features to predict a node-classification map.•We qualitatively demonstrate our method by illustrating the learnt label and node features.•We quantitatively demonstrate the benefit of using our global label features approach on 12 real-world datasets.