探索边嵌入对图神经网络的提升作用，发现其可显著减少模型深度。
Towards characterizing the value of edge embeddings in Graph Neural Networks
- 引入边嵌入机制，让模型更高效处理图结构数据
- 理论证明在特定任务下可大幅降低网络深度要求
- 在枢纽节点密集的图中效果尤其明显，适合复杂拓扑建模
图神经网络（GNNs）是解决图上机器学习问题的主流方法。尽管近年来已有大量理论与实证研究，但对GNN架构设计中更细粒度要素的理解仍不充分。本文探讨保持并更新边嵌入的架构优势。理论上，在合适的计算抽象和嵌入内存约束下，我们证明了在某些图模型任务中，使用边嵌入的架构可远比不使用的更浅。该技术受理论计算机科学中时间-空间权衡结果启发。实证上，维护边嵌入的架构几乎总优于仅依赖节点嵌入的版本，尤其在具有‘枢纽’节点的拓扑结构中表现显著提升。
原文摘要 · Abstract (English)
Graph neural networks (GNNs) are the dominant approach to solving machine learning problems defined over graphs. Despite much theoretical and empirical work in recent years, our understanding of finer-grained aspects of architectural design for GNNs remains impoverished. In this paper, we consider the benefits of architectures that maintain and update edge embeddings. On the theoretical front, under a suitable computational abstraction for a layer in the model, as well as memory constraints on the embeddings, we show that there are natural tasks on graphical models for which architectures leveraging edge embeddings can be much shallower. Our techniques are inspired by results on time-space tradeoffs in theoretical computer science. Empirically, we show architectures that maintain edge embeddings almost always improve on their node-based counterparts -- frequently significantly so in topologies that have ``hub'' nodes.
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