论文标题
图形神经网络上的统一视图作为图形信号
A Unified View on Graph Neural Networks as Graph Signal Denoising
论文作者
论文摘要
图形神经网络（GNN）在图形结构化数据的学习表示方面已提高。单个GNN层通常由特征转换和特征聚合操作组成。前者通常使用前馈网络来改变特征，而后者则在图表上汇总了转换的功能。最近的许多作品提出了在聚合操作中具有不同设计的GNN模型。在这项工作中，我们从数学上确定一组代表性GNN模型中的聚合过程包括GCN，GAT，PPNP和APPNP，可以被视为（近似）用平滑度假设解决图形降解问题。跨GNN的这种统一观点不仅为了解各种聚合操作提供了一个新的观点，还使我们能够开发一个统一的图形神经网络框架UGNN。为了证明其有希望的潜力，我们实例化了一种新型的GNN模型Ada-ugnn，该模型衍生自UGNN，以处理跨节点具有自适应平滑度的图形。全面的实验显示了ADA-UGNN的有效性。
Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. A single GNN layer typically consists of a feature transformation and a feature aggregation operation. The former normally uses feed-forward networks to transform features, while the latter aggregates the transformed features over the graph. Numerous recent works have proposed GNN models with different designs in the aggregation operation. In this work, we establish mathematically that the aggregation processes in a group of representative GNN models including GCN, GAT, PPNP, and APPNP can be regarded as (approximately) solving a graph denoising problem with a smoothness assumption. Such a unified view across GNNs not only provides a new perspective to understand a variety of aggregation operations but also enables us to develop a unified graph neural network framework UGNN. To demonstrate its promising potential, we instantiate a novel GNN model, ADA-UGNN, derived from UGNN, to handle graphs with adaptive smoothness across nodes. Comprehensive experiments show the effectiveness of ADA-UGNN.
