Graph neural networks (GNN) have been an active area of machine learning research to tackle various problems in graph data. A graph is a powerful way of representing relationships among entities as nodes connected by edges. Sometimes nodes and edges can have spatial features, such as 3D coordinates of nodes and directions along edges. How do we reason over the topology of graphs while considering those geometric features? In this post, we discuss a paper published in ICLR 2021:
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