Before revision: Analyzing graphs by representing them in a low dimensional space using Graph Neural Networks (GNNs) is a promising research problem, with a lot of ongoing research.
After revision: Learning feature space node embeddings that encode the position of a node within the context of a graph is useful in several graph prediction tasks. Majority of the existing graph neural networks (GNN) learn node embeddings that encode their local neighborhoods but not their positions. Consequently, two nodes that are vastly distant but located in similar local neighborhoods would map to similar embeddings. This limitation may prevent accurate performance in predictive tasks that rely on position information.