NODE-SELECT：一种基于选择传播技术的图神经网络
NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique
摘要：
虽然存在用于节点分类的各种各样的图神经网络（GNN），但是其中只有少数采用在消息传递过程中有效地针对噪声传播的机制。此外，显著影响图神经网络的一个非常重要的挑战是可扩展性问题，这限制了它们在更大图中的应用。在本文中，我们提出了我们的方法命名为NODE-SELECT：一个有效的图神经网络，使用子集层，只允许最好的共享拟合节点来传播他们的信息。通过在每个层中并行堆叠选择机制，我们提出的方法NODE-SELECT能够减少传播的噪声量，并适应现实世界中观察到的限制性共享概念。我们的NODE-SELECT在噪声实验中显著优于现有的GNN框架，并在不同基准数据集上的无噪声实验中匹配最先进的结果。
英文摘要：
While there exists a wide variety of graph neural networks (GNN) for node classification, only a minority of them adopt mechanisms that effectively target noise propagation during the message-passing procedure. Additionally, a very important challenge that significantly affects graph neural networks is the issue of scalability which limits their application to larger graphs. In this paper we propose our method named NODE-SELECT: an efficient graph neural network that uses subsetting layers which only allow the best sharing-fitting nodes to propagate their information. By having a selection mechanism within each layer which we stack in parallel, our proposed method NODE-SELECT is able to both reduce the amount noise propagated and adapt the restrictive sharing concept observed in real world graphs. Our NODE-SELECT significantly outperformed existing GNN frameworks in noise experiments and matched state-of-the art results in experiments without noise over different benchmark datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
