论文标题
数据驱动的几何散射网络学习
Data-Driven Learning of Geometric Scattering Networks
论文作者
论文摘要
我们基于最近提出的几何散射变换的松弛，提出了一个新的图神经网络（GNN）模块，该模块由图形小波滤波器组成。我们可学习的几何散射（腿）模块可以使小波的自适应调整能够鼓励乐队通道特征在学习的表示中出现。与许多流行的GNN相比，我们的腿部模块在GNN中的掺入可以学习长期图形关系，这些GNN通常依赖于邻居之间的平滑度或相似性来编码图形结构。此外，与竞争的GNN相比，其小波先验会导致简化的体系结构，学到的参数明显较少。我们证明了基于腿的网络在图形分类基准上的预测性能，以及在生化图数据探索任务中学习特征的描述性质量。
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations. The incorporation of our LEGS-module in GNNs enables the learning of longer-range graph relations compared to many popular GNNs, which often rely on encoding graph structure via smoothness or similarity between neighbors. Further, its wavelet priors result in simplified architectures with significantly fewer learned parameters compared to competing GNNs. We demonstrate the predictive performance of LEGS-based networks on graph classification benchmarks, as well as the descriptive quality of their learned features in biochemical graph data exploration tasks.
