论文标题
三维（3D）图的距离几何卷积网络（DG-GCN）
Distance-Geometric Graph Convolutional Network (DG-GCN) for Three-Dimensional (3D) Graphs
论文作者
论文摘要
距离几何图表示代表三维（3D）图的几何形状的统一方案（距离）。它对于图形的旋转和翻译是不变的，它反映了成对的节点相互作用及其通常的局部性质。为了促进3D图上的深度学习中的结合，我们提出了一个基于距离几何图表示：DG-GCN（距离几何图形卷积网络）。它利用连续滤波器卷积层和滤波器生成网络，从而使滤波器的权重从距离学习，从而将3D图的几何形状纳入图中。我们对ESOL和FREESOLV数据集的结果对标准图卷积的结果有了重大改进。它们还显示出使用边缘重量 /边缘距离功率定律的几何图卷积的显着改善。我们的工作证明了DG-GCN在3D图（尤其是分子图）上端到端深度学习的效用和价值。
The distance-geometric graph representation adopts a unified scheme (distance) for representing the geometry of three-dimensional(3D) graphs. It is invariant to rotation and translation of the graph and it reflects pair-wise node interactions and their generally local nature. To facilitate the incorporation of geometry in deep learning on 3D graphs, we propose a message-passing graph convolutional network based on the distance-geometric graph representation: DG-GCN (distance-geometric graph convolution network). It utilizes continuous-filter convolutional layers, with filter-generating networks, that enable learning of filter weights from distances, thereby incorporating the geometry of 3D graphs in graph convolutions. Our results for the ESOL and FreeSolv datasets show major improvement over those of standard graph convolutions. They also show significant improvement over those of geometric graph convolutions employing edge weight / edge distance power laws. Our work demonstrates the utility and value of DG-GCN for end-to-end deep learning on 3D graphs, particularly molecular graphs.
