论文标题
测量和改善图形神经网络中图信息的使用
Measuring and Improving the Use of Graph Information in Graph Neural Networks
论文作者
论文摘要
图形神经网络（GNN）已被广泛用于表示图数据的表示。但是，人们对图形数据的实际绩效GNN的实际收益有限。本文介绍了一个符合上下文的GNN框架，并提出了两个平滑度指标，以测量从图形数据获得的信息的数量和质量。然后，一种称为CS-GNN的新型GNN模型旨在根据图的平滑度值改善图形信息的使用。证明CS-GNN比不同类型的真实图中现有方法获得更好的性能。
Graph neural networks (GNNs) have been widely used for representation learning on graph data. However, there is limited understanding on how much performance GNNs actually gain from graph data. This paper introduces a context-surrounding GNN framework and proposes two smoothness metrics to measure the quantity and quality of information obtained from graph data. A new GNN model, called CS-GNN, is then designed to improve the use of graph information based on the smoothness values of a graph. CS-GNN is shown to achieve better performance than existing methods in different types of real graphs.
