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，但通常通过在大规模全局图上使用专用图神经网络来处理。这些模型需要大量的内存和计算资源，却难以建模子图的层级结构。本文提出子图到节点（S2N）翻译这一新颖方法，用于学习子图表示。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。理论和实证均表明，与最先进模型相比，S2N不仅显著降低了内存和计算成本，还通过捕获子图的局部和全局结构实现了更优性能。通过利用图粗化方法，即使在子图数量不足的数据稀缺场景下，我们的方法也优于基线模型。在八个基准测试上的实验表明，经过S2N翻译微调的模型能以更优或相当的性能水平，处理比最先进模型多183至711倍的子图样本。