论文标题
非本地图神经网络
Non-Local Graph Neural Networks
论文作者
论文摘要
现代图神经网络（GNN）通过多层局部聚合学习节点嵌入，并在分类图应用程序上取得了巨大成功。但是，拆卸图上的任务通常需要非本地聚集。此外，我们发现局部聚集甚至对某些拆卸图有害。在这项工作中，我们提出了一个简单而有效的非本地聚合框架，并对GNN进行了有效的注意引导分类。基于它，我们开发了各种非本地GNN。我们进行彻底的实验，以分析拆卸图数据集并评估我们的非本地GNN。实验结果表明，就模型性能和效率而言，我们的非本地GNN在七个基准图表的七个基准数据集上显着优于先前的最先前方法。
Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs. However, tasks on disassortative graphs usually require non-local aggregation. In addition, we find that local aggregation is even harmful for some disassortative graphs. In this work, we propose a simple yet effective non-local aggregation framework with an efficient attention-guided sorting for GNNs. Based on it, we develop various non-local GNNs. We perform thorough experiments to analyze disassortative graph datasets and evaluate our non-local GNNs. Experimental results demonstrate that our non-local GNNs significantly outperform previous state-of-the-art methods on seven benchmark datasets of disassortative graphs, in terms of both model performance and efficiency.
