详解图神经网络核心机制与训练陷阱，助工程师避坑。
Introduction to Graph Neural Networks for Machine Learning Engineers
- 以编码器-解码器框架解析GNN工作原理
- 实证揭示训练数据少时易过平滑、复杂图易过挤压
- 适合刚接触GNN的机器学习工程师快速上手
图神经网络是为带属性的节点或边的图设计的深度神经网络。由于在多种任务上表现优异，相关研究论文数量迅速增长。本文通过编码器-解码器框架介绍图神经网络，并给出多种图分析任务的解码器实例。结合理论分析和大量在同质图上的实验，探讨了不同训练规模和图复杂度下GNN的行为特征，重点分析过平滑与过挤压现象。
原文摘要 · Abstract (English)
Graph neural networks are deep neural networks designed for graphs with attributes attached to nodes or edges. The number of research papers in the literature concerning these models is growing rapidly due to their impressive performance on a broad range of tasks. This survey introduces graph neural networks through the encoder-decoder framework and provides examples of decoders for a range of graph analytic tasks. It uses theory and numerous experiments on homogeneous graphs to illustrate the behavior of graph neural networks under different training sizes and degrees of graph complexity, with an emphasis on oversmoothing and oversquashing.
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