Efficient Representation Learning of Subgraphs by Subgraph-To-Node Translation

Dongkwan Kim, Alice Oh

Keywords: Efficiency, Subgraphs, Graph Neural Networks

TL;DR: We propose Subgraph-To-Node translation to efficiently learn representations of subgraphs by coarsely translating subgraphs into nodes.

Abstract: A subgraph is a data structure that can represent various real-world problems. We propose Subgraph-To-Node (S2N) translation, which is a novel formulation to efficiently learn representations of subgraphs. Specifically, given a set of subgraphs in the global graph, we construct a new graph by coarsely transforming subgraphs into nodes. We perform subgraph-level tasks as node-level tasks through this translation. By doing so, we can significantly reduce the memory and computational costs in both training and inference. We conduct experiments on four real-world datasets to evaluate performance and efficiency. Our experiments demonstrate that models with S2N translation are more efficient than state-of-the-art models without substantial performance decrease.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/efficient-representation-learning-of/code)