标题： 图U-Net 显示英文标题

标题： Graph U-Nets

摘要： 我们研究了图数据的表征学习问题。卷积神经网络可以自然地处理图像，但在处理图数据时面临重大挑战。由于图像可以看作节点位于二维网格上的特殊图，图嵌入任务与像素级预测任务（如分割）有自然的对应关系。虽然像U-Net这样的编码器-解码器架构已在许多像素级预测任务中成功应用，但对于图数据来说，类似的方法还很缺乏。这是因为池化和上采样操作在图数据上并不自然。为了解决这些挑战，本研究提出了新颖的图池化(gPool)和图解池化(gUnpool)操作。gPool层根据节点在可训练投影向量上的标量投影值自适应选择一些节点以形成更小的图。我们进一步提出了gUnpool层作为gPool层的逆操作。gUnpool层利用相应gPool层中选择节点的位置信息恢复图的原始结构。基于提出的gPool和gUnpool层，我们在图上开发了一种编码器-解码器模型，称为图U-Net。我们的节点分类和图分类任务的实验结果表明，我们的方法比以前的模型表现得更好。 显示英文摘要

摘要： We consider the problem of representation learning for graph data. Convolutional neural networks can naturally operate on images, but have significant challenges in dealing with graph data. Given images are special cases of graphs with nodes lie on 2D lattices, graph embedding tasks have a natural correspondence with image pixel-wise prediction tasks such as segmentation. While encoder-decoder architectures like U-Nets have been successfully applied on many image pixel-wise prediction tasks, similar methods are lacking for graph data. This is due to the fact that pooling and up-sampling operations are not natural on graph data. To address these challenges, we propose novel graph pooling (gPool) and unpooling (gUnpool) operations in this work. The gPool layer adaptively selects some nodes to form a smaller graph based on their scalar projection values on a trainable projection vector. We further propose the gUnpool layer as the inverse operation of the gPool layer. The gUnpool layer restores the graph into its original structure using the position information of nodes selected in the corresponding gPool layer. Based on our proposed gPool and gUnpool layers, we develop an encoder-decoder model on graph, known as the graph U-Nets. Our experimental results on node classification and graph classification tasks demonstrate that our methods achieve consistently better performance than previous models.