Euclidean geometry meets graph, a geometric deep learning perspective

Anonymous

Keywords: graph neural network, geometric deep learning

Abstract: Graph neural networks (GNN) have been an active area of machine learning research to tackle various problems in graph data. Sometimes nodes and edges in a graph 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 ICLR 2021 paper "Learning from Protein Structure with Geometric Vector Perceptrons (GVP)". We take a geometric deep learning perspective to explain the intuition and advantage of GVP-GNN, the novel GNN architecture described in the paper, to reason over geometric graphs. We inspire an idea along physics-oriented interpretation. We also highlight under-appreciated applications of geometric graph learning in solving problems from multiple domains, including biochemistry, geography, and network analysis.

ICLR Paper: https://openreview.net/forum?id=1YLJDvSx6J4