Subgraph representation learning has emerged as an important problem, but it is by default approached with specialized graph neural networks on a large global graph. These models demand extensive memory and computational resources but challenge modeling hierarchical structures of subgraphs. In this paper, we propose Subgraph-To-Node (S2N) translation, a novel formulation for learning representations of subgraphs. Specifically, given a set of subgraphs in the global graph, we construct a new graph by coarsely transforming subgraphs into nodes. Demonstrating both theoretical and empirical evidence, S2N not only significantly reduces memory and computational costs compared to state-of-the-art models but also outperforms them by capturing both local and global structures of the subgraph. By leveraging graph coarsening methods, our method outperforms baselines even in a data-scarce setting with insufficient subgraphs. Our experiments on eight benchmarks demonstrate that fined-tuned models with S2N translation can process 183 -- 711 times more subgraph samples than state-of-the-art models at a better or similar performance level.

翻译：子图表示学习已成为一个重要问题，但默认情况下需借助大型全局图上的专用图神经网络来解决。这些模型需要大量内存和计算资源，却难以建模子图的层次结构。本文提出子图到节点（S2N）转换，这是一种用于学习子图表示的新颖框架。具体而言，给定全局图中的一组子图，我们通过粗粒度地将子图转换为节点来构建新图。理论和实证证据表明，与最先进模型相比，S2N不仅能显著降低内存和计算成本，还能通过捕获子图的局部和全局结构实现更优性能。借助图粗化方法，即使在子图不足的数据稀缺场景中，我们的方法也优于基线模型。在八个基准上的实验证明，采用S2N转换的微调模型能以更优或相似的性能水平，处理比最先进模型多183至711倍的子图样本。