论文标题
几何图表和几何图形卷积，用于三维（3D）图的深度学习
Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on Three-Dimensional (3D) Graphs
论文作者
论文摘要
由节点和边缘组成的三维（3D）图的几何形状在许多重要应用中起着至关重要的作用。一个很好的例子是分子图，其几何形状会影响分子的重要特性，包括其反应性和生物学活性。为了促进3D图上的深度学习中的结合，我们定义了三种类型的几何图表示：位置，角度几何和距离几何。为了获得概念证明，我们将距离几何图表示用于几何图卷积。此外，为了利用标准图卷积网络，我们采用了一个简单的边缘权重 /边缘距离相关方案，可以使用参考值固定参数或通过贝叶斯高参数优化确定。对于ESOL和FREESOL数据集的几何图卷积的结果比标准图卷积的结果显着改善。我们的工作证明了在3D图上的深度学习中，使用距离几何图表示结合几何形状的可行性和希望。
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.
