  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.
