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- W4295612437 abstract "The geometry of three-dimensional (3D) graphs, consisting of nodes and edges, plays a crucial role in many important applications. An excellent example is molecular graphs, whose geometry influences important properties of a molecule including its reactivity and biological activity. To facilitate the incorporation of geometry in deep learning on 3D graphs, we define three types of geometric graph representations: positional, angle-geometric and distance-geometric. For proof of concept, we use the distance-geometric graph representation for geometric graph convolutions. Further, to utilize standard graph convolution networks, we employ a simple edge weight / edge distance correlation scheme, whose parameters can be fixed using reference values or determined through Bayesian hyperparameter optimization. The results of geometric graph convolutions, for the ESOL and Freesol datasets, show significant improvement over those of standard graph convolutions. Our work demonstrates the feasibility and promise of incorporating geometry, using the distance-geometric graph representation, in deep learning on 3D graphs." @default.
- W4295612437 created "2022-09-14" @default.
- W4295612437 creator A5066999133 @default.
- W4295612437 date "2020-06-02" @default.
- W4295612437 modified "2023-09-30" @default.
- W4295612437 title "Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on Three-Dimensional (3D) Graphs" @default.
- W4295612437 doi "https://doi.org/10.48550/arxiv.2006.01785" @default.
- W4295612437 hasPublicationYear "2020" @default.
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