论文标题
摆脱悬浮动画问题：图形半监督分类的深度扩散神经网络
Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised Classification
论文作者
论文摘要
当模型体系结构深入时，现有的图形神经网络可能会遭受“悬浮动画问题”。同时，对于某些图形学习方案，例如，具有文本/图像属性的节点或具有长距离节点相关的图形，对于有效的图形表示学习将是必需的深图神经网络。在本文中，我们提出了一个新的图神经网络，即difnet（图扩散神经网络），用于图表示学习和节点分类。 Difnet利用神经门和图形残留学习来进行节点隐藏的状态建模，并包括一个用于节点邻域信息扩散的注意机制。将在本文中进行广泛的实验，以将DIFNET与几个最新的图形神经网络模型进行比较。实验结果可以说明DIFNET的学习绩效优势和有效性，尤其是在解决“暂停动画问题”时。
Existing graph neural networks may suffer from the "suspended animation problem" when the model architecture goes deep. Meanwhile, for some graph learning scenarios, e.g., nodes with text/image attributes or graphs with long-distance node correlations, deep graph neural networks will be necessary for effective graph representation learning. In this paper, we propose a new graph neural network, namely DIFNET (Graph Diffusive Neural Network), for graph representation learning and node classification. DIFNET utilizes both neural gates and graph residual learning for node hidden state modeling, and includes an attention mechanism for node neighborhood information diffusion. Extensive experiments will be done in this paper to compare DIFNET against several state-of-the-art graph neural network models. The experimental results can illustrate both the learning performance advantages and effectiveness of DIFNET, especially in addressing the "suspended animation problem".
