论文标题
等距图神经网络
Isometric Graph Neural Networks
论文作者
论文摘要
如果这些表示形式忠于图表中的节点之间的距离，那么依赖图中节点表示的许多任务将受益。提取此类表示形式的几何技术在较大的图表上的缩放量很差，并且图形神经网络（GNN）算法的最新进展具有有限的能力，无法反映出图形距离信息以外的距离邻居。为了启用这种高度期望的能力，我们提出了一种学习等距图神经网络（IGNN）的技术，该技术需要更改输入表示空间和损耗函数，以使任何GNN算法能够生成反映节点之间距离的表示。我们在多个GNN架构上尝试使用等距技术，以建模多个数据集上的多个预测任务。除了这些实验中的AUC-ROC高达$ 43 \％$的改善外，我们还观察到Kendall's Tau（KT）的一致和实质性改善高达400％，该措施直接反映了距离信息，表明所学到的嵌入式嵌入确实考虑了图形距离。
Many tasks that rely on representations of nodes in graphs would benefit if those representations were faithful to distances between nodes in the graph. Geometric techniques to extract such representations have poor scaling over large graph size, and recent advances in Graph Neural Network (GNN) algorithms have limited ability to reflect graph distance information beyond the first degree neighborhood. To enable this highly desired capability, we propose a technique to learn Isometric Graph Neural Networks (IGNN), which requires changing the input representation space and loss function to enable any GNN algorithm to generate representations that reflect distances between nodes. We experiment with the isometric technique on several GNN architectures for modeling multiple prediction tasks on multiple datasets. In addition to an improvement in AUC-ROC as high as $43\%$ in these experiments, we observe a consistent and substantial improvement as high as 400% in Kendall's Tau (KT), a measure that directly reflects distance information, demonstrating that the learned embeddings do account for graph distances.
